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

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.

Comprehensive conservation assessments reveal high extinction risks across Atlantic Forest trees

Biodiversity is declining globally, yet many biodiversity hotspots still lack comprehensive species conservation assessments. Using multiple International Union for Conservation of Nature (IUCN) Red List criteria to evaluate extinction risks and millions of herbarium and forest inventory records, we present automated conservation assessments for all tree species of the Atlantic Forest biodiversity hotspot, including ~1100 heretofore unassessed species. About 65% of all species and 82% of endemic species are classified as threatened. We rediscovered five species classified as Extinct on the IUCN Red List and identified 13 endemics as possibly extinct. Uncertainties in species information had little influence on the assessments, but using fewer Red List criteria severely underestimated threat levels. We suggest that the conservation status of tropical forests worldwide is worse than previously reported.

Retrieval of long DNA reads from herbarium specimens

High-throughput sequencing of herbarium specimens' DNA with short-read platforms has helped explore many biological questions. Here, for the first time, we investigate the potential of using herbarium specimens as a resource for long-read DNA sequencing technologies. We use target capture of 48 low-copy nuclear loci in 12 herbarium specimens of Silene as a basis for long-read sequencing using SMRT PacBio Sequel. The samples were collected between 1932 and 2019. A simple optimization of size selection protocol enabled the retrieval of both long DNA fragments (>1 kb) and long on-target reads for nine of them. The limited sampling size does not enable statistical evaluation of the influence of specimen age to the DNA fragmentation, but our results confirm that younger samples, that is, collected after 1990, are less fragmented and have better sequencing success than specimens collected before this date. Specimens collected between 1990 and 2019 yield between 167 and 3403 on-target reads > 1 kb. They enabled recovering between 34 loci and 48 (i.e. all loci recovered). Three samples from specimens collected before 1990 did not yield on-target reads > 1 kb. The four other samples collected before this date yielded up to 144 reads and recovered up to 25 loci. Young herbarium specimens seem promising for long-read sequencing. However, older ones have partly failed. Further exploration would be necessary to statistically test and understand the potential of older material in the quest for long reads. We would encourage greatly expanding the sampling size and comparing different taxonomic groups.

Prioritizing the reassessment of data-deficient species on the IUCN Red List

Despite being central to the implementation of conservation policies, the usefulness of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species is hampered by the 14% of species classified as data-deficient (DD) because information to evaluate these species' extinction risk was lacking when they were last assessed or because assessors did not appropriately account for uncertainty. Robust methods are needed to identify which DD species are more likely to be reclassified in one of the data-sufficient IUCN Red List categories. We devised a reproducible method to help red-list assessors prioritize reassessment of DD species and tested it with 6887 DD species of mammals, reptiles, amphibians, fishes, and Odonata (dragonflies and damselflies). For each DD species in these groups, we calculated its probability of being classified in a data-sufficient category if reassessed today from covariates measuring available knowledge (e.g., number of occurrence records or published articles available), knowledge proxies (e.g., remoteness of the range), and species characteristics (e.g., nocturnality); calculated change in such probability since last assessment from the increase in available knowledge (e.g., new occurrence records); and determined whether the species might qualify as threatened based on recent rate of habitat loss determined from global land-cover maps. We identified 1907 species with a probability of being reassessed in a data-sufficient category of >0.5; 624 species for which this probability increased by >0.25 since last assessment; and 77 species that could be reassessed as near threatened or threatened based on habitat loss. Combining these 3 elements, our results provided a list of species likely to be data-sufficient such that the comprehensiveness and representativeness of the IUCN Red List can be improved.

Botanical Collection Patterns and Conservation Categories of the Most Traded Timber Species from the Ecuadorian Amazon: The Role of Protected Areas

The Ecuadorian Amazon is home to a rich biodiversity of woody plant species. Nonetheless, their conservation remains difficult, as some areas remain poorly explored and lack georeferenced records. Therefore, the current study aims predominantly to analyze the collection patterns of timber species in the Amazon lowlands of Ecuador and to evaluate the conservation coverage of these species in protected areas. Furthermore, we try to determine the conservation category of the species according to the criteria of the IUCN Red List. We identified that one third of the timber species in the study area was concentrated in three provinces due to historical botanical expeditions. However, a worrying 22.0% of the species had less than five records of presence, and 29.9% had less than ten records, indicating a possible underestimation of their presence. In addition, almost half of the species evaluated were unprotected, exposing them to deforestation risks and threats. To improve knowledge and conservation of forest biodiversity in the Ecuadorian Amazon, it is recommended to perform new botanical samplings in little-explored areas and digitize data in national herbaria. It is critical to implement automated assessments of the conservation status of species with insufficient data. In addition, it is suggested to use species distribution models to identify optimal areas for forest restoration initiatives. Effective communication of results and collaboration between scientists, governments, and local communities are key to the protection and sustainable management of forest biodiversity in the Amazon region.

FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes

Premise

Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers.

Methods and Results

Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi-based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device.

Conclusions

FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.

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.

Implications for conservation assessment from flux in the botanical record over 20 years in southwest Ghana

At best, conservation decisions can only be made using the data available at the time. For plants and especially in the tropics, natural history collections remain the best available baseline information upon which to base conservation assessments, in spite of well-documented limitations in their taxonomic, geographic, and temporal coverage. We explore the extent to which changes to the plant biological record over 20 years have changed our conception of the conservation importance of 931 plant taxa, and 114 vegetation samples, recorded in forest reserves of the southwest Ghana biodiversity hotspot. 36% of species-level assessments changed as a result of new distribution data. 12% of species accepted in 2016 had no assessment in 1996: of those, 20% are new species publications, 60% are new records for SW Ghana, and 20% are taxonomic resolutions. Apparent species ranges have increased over time as new records are made, but new species publications are overwhelmingly of globally rare species, keeping the balance of perceived rarity in the flora constant over 20 years. Thus, in spite of considerable flux at the species record level, range size rarity scores calculated for 114 vegetation samples of the reserves in 1996 and 2016 are highly correlated with each other: r(112) = 0.84, p < .0005, and showed no difference in mean score over 20 years: paired t(113) = -0.482, p = .631. This consistency in results at the area level allows for worthwhile conservation priority setting over time, and we argue is the better course of action than taking no action at all.

Systematics of the Trembleya sensu stricto clade of Microlicia (Melastomataceae, Lavoisiereae)

A systematic monograph of the Trembleya s.s. clade is presented, a Brazilian endemic lineage of Melastomataceae comprising 11 species and currently recognised as part of Microlicia s.l. (Melastomataceae). First, we investigate phylogenetic relationships within Lavoisiereae using two nuclear markers and two sampling datasets (102 and 134 terminals). Then, we provide a systematic revision and new circumscription of the Trembleya s.s. clade, including line drawings, photos of living specimens, leaves and floral parts, distribution maps, a key to the 11 accepted species, comments on morphology, reproductive biology, richness, endemism, biogeography and recommended conservation assessments. A nomenclatural update of all taxa previously treated in Trembleya is also provided, including the designation of 45 lectotypes and the proposal of 38 new synonyms.

The likely extinction of hundreds of palm species threatens their contributions to people and ecosystems

Protecting nature's contributions to people requires accelerating extinction risk assessment and better integrating evolutionary, functional and used diversity with conservation planning. Here, we report machine learning extinction risk predictions for 1,381 palm species (Arecaceae), a plant family of high socio-economic and ecological importance. We integrate these predictions with published assessments for 508 species (covering 75% of all palm species) and we identify top-priority regions for palm conservation on the basis of their proportion of threatened evolutionarily distinct, functionally distinct and used species. Finally, we explore palm use resilience to identify non-threatened species that could potentially serve as substitutes for threatened used species by providing similar products. We estimate that over a thousand palms (56%) are probably threatened, including 185 species with documented uses. Some regions (New Guinea, Vanuatu and Vietnam) emerge as top ten priorities for conservation only after incorporating machine learning extinction risk predictions. Potential substitutes are identified for 91% of the threatened used species and regional use resilience increases with total palm richness. However, 16 threatened used species lack potential substitutes and 30 regions lack substitutes for at least one of their threatened used palm species. Overall, we show that hundreds of species of this keystone family face extinction, some of them probably irreplaceable, at least locally. This highlights the need for urgent actions to avoid major repercussions on palm-associated ecosystem processes and human livelihoods in the coming decades.

The ECAT dataset: expert-validated distribution data of endemic and sub-endemic trees of Central Africa (Dem. Rep. Congo, Rwanda, Burundi)

In this data paper, we present a specimen-based occurrence dataset compiled in the framework of the Conservation of Endemic Central African Trees (ECAT) project with the aim of producing global conservation assessments for the IUCN Red List. The project targets all tree species endemic or sub-endemic to the Central African region comprising the Democratic Republic of the Congo (DR Congo), Rwanda, and Burundi. The dataset contains 6361 plant collection records with occurrences of 8910 specimens from 337 taxa belonging to 153 genera in 52 families. Many of these tree taxa have restricted geographic ranges and are only known from a small number of herbarium specimens. As assessments for such taxa can be compromised by inadequate data, we transcribed and geo-referenced specimen label information to obtain a more accurate and complete locality dataset. All specimen data were manually cleaned and verified by botanical experts, resulting in improved data quality and consistency.

Reshi. Taxonomy and threat assessment of Lagotis kunawurensis Rupr. (Plantaginaceae), an endemic medicinal plant species of the Himalaya, India

Lagotis kunawurensis Rupr. (Plantaginaceae), a rare plant species endemic to the Himalaya, is reported here after a gap of 50 years from Ladakh. This species has often been taxonomically misidentified and confused with Picrorhiza kurroa, an important medicinal plant of the Himalaya. The present study clarifies the taxonomy of L. kunawurensis by providing description and photo illustrations of diagnostic characters which will aid its proper field identification. Furthermore, the threat assessment of L. kunawurensis using the IUCN Red List of Threatened Species has been conducted based on the available occurrence records, and the species currently falls under the ‘Near Threatened’ category. This species is used for medicinal purposes by locals in the study area. As the species is simultaneously experiencing various kinds of threats and the known distribution range is relatively smaller, it is right time to develop conservation strategies for the sustainable utilization of this endemic medicinal plant species of the Himalaya.

Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence

Trees are fundamental for Earth's biodiversity as primary producers and ecosystem engineers and are responsible for many of nature's contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is necessary to quantify the current biodiversity crisis and to prioritize conservation efforts. However, the most comprehensive dataset of tree species extinction risk-the Red List of the International Union for the Conservation of Nature (IUCN RL)-lacks assessments for a substantial number of known tree species. The RL is based on a time-consuming expert-based assessment process, which hampers the inclusion of less-known species and the continued updating of extinction risk assessments. In this study, we used a computational pipeline to approximate RL extinction risk assessments for more than 21,000 tree species (leading to an overall assessment of 89% of all known tree species) using a supervised learning approach trained based on available IUCN RL assessments. We harvested the occurrence data for tree species worldwide from online databases, which we used with other publicly available data to design features characterizing the species' geographic range, biome and climatic affinities, and exposure to human footprint. We trained deep neural network models to predict their conservation status, based on these features. We estimated 43% of the assessed tree species to be threatened with extinction and found taxonomic and geographic heterogeneities in the distribution of threatened species. The results are consistent with the recent estimates by the Global Tree Assessment initiative, indicating that our approach provides robust and time-efficient approximations of species' IUCN RL extinction risk assessments.

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.

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.

A strategy for the next decade to address data deficiency in neglected biodiversity

Measuring progress toward international biodiversity targets requires robust information on the conservation status of species, which the International Union for Conservation of Nature (IUCN) Red List of Threatened Species provides. However, data and capacity are lacking for most hyperdiverse groups, such as invertebrates, plants, and fungi, particularly in megadiverse or high-endemism regions. Conservation policies and biodiversity strategies aimed at halting biodiversity loss by 2020 need to be adapted to tackle these information shortfalls after 2020. We devised an 8-point strategy to close existing data gaps by reviving explorative field research on the distribution, abundance, and ecology of species; linking taxonomic research more closely with conservation; improving global biodiversity databases by making the submission of spatially explicit data mandatory for scientific publications; developing a global spatial database on threats to biodiversity to facilitate IUCN Red List assessments; automating preassessments by integrating distribution data and spatial threat data; building capacity in taxonomy, ecology, and biodiversity monitoring in countries with high species richness or endemism; creating species monitoring programs for lesser-known taxa; and developing sufficient funding mechanisms to reduce reliance on voluntary efforts. Implementing these strategies in the post-2020 biodiversity framework will help to overcome the lack of capacity and data regarding the conservation status of biodiversity. This will require a collaborative effort among scientists, policy makers, and conservation practitioners.

Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

Although often not collected specifically for the purposes of conservation, herbarium specimens offer sufficient information to reconstruct parameters that are needed to designate a species as 'at-risk' of extinction. While such designations should prompt quick and efficient legal action towards species recovery, such action often lags far behind and is mired in bureaucratic procedure. The increase in online digitization of natural history collections has now led to a surge in the number new studies on the uses of machine learning. These repositories of species occurrences are now equipped with advances that allow for the identification of rare species. The increase in attention devoted to estimating the scope and severity of the threats that lead to the decline of such species will increase our ability to mitigate these threats and reverse the declines, overcoming a current barrier to the recovery of many threatened plant species. Thus far, collected specimens have been used to fill gaps in systematics, range extent, and past genetic diversity. We find that they also offer material with which it is possible to foster species recovery, ecosystem restoration, and de-extinction, and these elements should be used in conjunction with machine learning and citizen science initiatives to mobilize as large a force as possible to counter current extinction trends.

Extinction Risk Assessment of the Greek Endemic Flora

Simple Summary

This study assesses for the first time all the vascular endemic plant taxa of Greece, according to their decline and rarity. Phylogenetic analysis and its spatial overview highlight areas for conservation prioritization. Several of the Greek endemics are threatened with extinction and fourteen of them need to be prioritized, due to their evolutionary distinctiveness. This assessment could act as the baseline and supporting tool for conservation actions, decision- and policy-making for biodiversity, while highlighting the need for a new Red Data Book for the Greek flora.

Abstract

Human-induced biodiversity decline has been on the rise for the past 250 years, due to various causes. What is equally troubling, is that we are unaware which plants are threatened and where they occur. Thus, we are far from reaching Aichi Biodiversity Target 2, i.e., assessing the extinction risk of most species. To that end, based on an extensive occurrence dataset, we performed an extinction risk assessment according to the IUCN Criteria A and B for all the endemic plant taxa occurring in Greece, one of the most biodiverse countries in Europe, in a phylogenetically-informed framework and identified the areas needing conservation prioritization. Several of the Greek endemics are threatened with extinction and fourteen endemics need to be prioritized, as they are evolutionary distinct and globally endangered. Mt. Gramos is identified as the most important conservation hotspot in Greece. However, a significant portion of the identified conservation hotspots is not included in any designated Greek protected area, meaning that the Greek protected areas network might need to be at least partially redesigned. In the Anthropocene era, where climate and land-use change are projected to alter biodiversity patterns and may force many species to extinction, our assessment provides the baseline for future conservation research, ecosystem services maintenance, and might prove crucial for the timely, systematic and effective aversion of plant extinctions in Greece.

Assessing extinction risk from geographic distribution data in Neotropical freshwater fishes

Abstract The IUCN Red List (RL) provides high-quality conservation assessments for individual species, yet the rate and scale of environmental deterioration globally challenges the conservation community to develop expedited methods for risk assessment. Here we compare threat assessments for 3,001 species of Neotropical freshwater fishes (NFF) in the IUCN–RL using readily accessible data types as proxies for extinction risk: geographic range, elevation, and species publication date. Furthermore, using geographic and taxonomic data alone, we generated preliminary conservation assessments for 2,334 NFF species currently awaiting IUCN assessment, identifying an additional 671 NFF species as potentially threatened. This number of potentially threatened species represents an increase of 59% over the number of species currently assigned to threat categories by the IUCN–RL. These results substantially expand the number of threatened NFF species from 422 currently on the IUCN RL to 1,093 species as threatened or potentially threatened, representing about 18% of all NFF species. Extinction risk is greater in species with smaller geographic ranges, which inhabit upland rivers, and which were described more recently. We propose the Central and Southern Andes, and Eastern Guiana Shield as priorities in the upcoming IUCN RL assessment of NFF species conservation risk.

Small herbaria contribute unique biogeographic records to county, locality, and temporal scales

PREMISE

With digitization and data sharing initiatives underway over the last 15 years, an important need has been prioritizing specimens to digitize. Because duplicate specimens are shared among herbaria in exchange and gift programs, we investigated the extent to which unique biogeographic data are held in small herbaria vs. these data being redundant with those held by larger institutions. We evaluated the unique specimen contributions that small herbaria make to biogeographic understanding at county, locality, and temporal scales.

METHODS

We sampled herbarium specimens of 40 plant taxa from each of eight states of the United States of America in four broad status categories: extremely rare, very rare, common native, and introduced. We gathered geographic information from specimens held by large (≥100,000 specimens) and small (<100,000 specimens) herbaria. We built generalized linear mixed models to assess which features of the collections may best predict unique contributions of herbaria and used an Akaike information criterion-based information-theoretic approach for our model selection to choose the best model for each scale.

RESULTS

Small herbaria contributed unique specimens at all scales in proportion with their contribution of specimens to our data set. The best models for all scales were the full models that included the factors of species status and herbarium size when accounting for state as a random variable.

CONCLUSIONS

We demonstrated that small herbaria contribute unique information for research. It is clear that unique contributions cannot be predicted based on herbarium size alone. We must prioritize digitization and data sharing from herbaria of all sizes.

No one-size-fits-all solution to clean GBIF

Species occurrence records provide the basis for many biodiversity studies. They derive from georeferenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic filtering is a scalable and reproducible means to identify potentially problematic records and tailor datasets from public databases such as the Global Biodiversity Information Facility (GBIF; http://www.gbif.org), for biodiversity analyses. However, it is unclear how much data may be lost by filtering, whether the same filters should be applied across all taxonomic groups, and what the effect of filtering is on common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa, including terrestrial and marine animals, fungi, and plants downloaded from GBIF. We find that a total of 44.3% of the records are potentially problematic, with large variation across taxonomic groups (25–90%). A small fraction of records was identified as erroneous in the strict sense (4.2%), and a much larger proportion as unfit for most downstream analyses (41.7%). Filters of duplicated information, collection year, and basis of record, as well as coordinates in urban areas, or for terrestrial taxa in the sea or marine taxa on land, have the greatest effect. Automated filtering can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough recording and exploration of the meta-data associated with species records for biodiversity research.

Natural history collections and the future legacy of ecological research

Natural history collections are now being championed as key to broad ecological studies, especially those involving human impacts in the Anthropocene. However, collections are going through a crisis that threatens their present and future value, going beyond underfunding/understaffing to a more damaging practice: current researchers are no longer depositing material. This seems to be especially true for ecological studies that now benefit from historical collections, as those researchers are not trained to think about voucher specimens. We investigated indexed journals in Ecology and Zoology to assess if they have guidelines concerning voucher specimens. Only 4% of ecological journals presently encourage (but mostly do not require) voucher deposition, while 15% of zoological journals encourage it. In the first place, this goes contrary to scientific standards of reproducibility, since specimens are primary data. Secondly, this erodes the legacy we will leave for future researchers, because if this trend goes on unchecked, it will leave a massive gap in collections' coverage, undermining the quality that is presently acclaimed. The scientific community needs a wakeup call to avoid impoverishing the future value of natural history collections. Training and changing researchers' mindsets is essential, but that takes time. For the moment, we propose a stopgap measure: at the minimum, academic journals should encourage authors to deposit specimens in open collections, such as museums and universities.

Rapid Least Concern: towards automating Red List assessments

Background

The IUCN Red List of Threatened Species^TM (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.

New information

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.

A third of the tropical African flora is potentially threatened with extinction

Preserving tropical biodiversity is an urgent challenge when faced with the growing needs of countries. Despite their crucial importance for terrestrial ecosystems, most tropical plant species lack extinction risk assessments, limiting our ability to identify conservation priorities. Using a novel approach aligned with IUCN Red List criteria, we conducted a continental-scale preliminary conservation assessment of 22,036 vascular plant species in tropical Africa. Our results underline the high level of extinction risk of the tropical African flora. Thirty-three percent of the species are potentially threatened with extinction, and another third of species are likely rare, potentially becoming threatened in the near future. Four regions are highlighted with a high proportion (>40%) of potentially threatened species: Ethiopia, West Africa, central Tanzania, and southern Democratic Republic of the Congo. Our approach represents a first step toward data-driven conservation assessments applicable at continental scales providing crucial information for sustainable economic development prioritization.

Biological collections for understanding biodiversity in the Anthropocene

Global change has become a central focus of modern biology. Yet, our knowledge of how anthropogenic drivers affect biodiversity and natural resources is limited by a lack of biological data spanning the Anthropocene. We propose that the hundreds of millions of plant, fungal and animal specimens deposited in natural history museums have the potential to transform the field of global change biology. We suggest that museum specimens are underused, particularly in ecological studies, given their capacity to reveal patterns that are not observable from other data sources. Increasingly, museum specimens are becoming mobilized online, providing unparalleled access to physiological, ecological and evolutionary data spanning decades and sometimes centuries. Here, we describe the diversity of collections data archived in museums and provide an overview of the diverse uses and applications of these data as discussed in the accompanying collection of papers within this theme issue. As these unparalleled resources are under threat owing to budget cuts and other institutional pressures, we aim to shed light on the unique discoveries that are possible in museums and, thus, the singular value of natural history collections in a period of rapid change.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.