IUCNN – Deep learning approaches to approximate species' extinction risk

Heathers (Erica, Ericaceae) of Madagascar: taxonomy, evolution, ecology and uses

The plant genus Erica L. (heathers; anjavidy in Malagasy) has 35 recognised species in Madagascar, but there has not been a taxonomic revision since 1927 and there are few identification resources. We review available data for Malagasy Erica (previously treated as Philippia), summarise diagnostic species descriptions and incorporate them into the Erica Identification Aid. There is clearly variation in current species concepts that requires further study. Malagasy Erica most likely represent a single clade also encompassing species from the Mascarenes, but resolution is poor and most species remain unsequenced. Erica is found in several of Madagascar's ecosystems, including the high-altitude "ericoid thickets" where diversity is highest, but it is absent from the extensive dry western areas. Habitats include the ericoid thickets, shrubland-grassland mosaics in the central highlands and on the eastern coast, and Uapacabojeri (tapia) savanna. Many Erica species are likely to be part of dynamic ecosystems with infrequent fire regimes. The palaeorecord indicates a more widespread ericoid shrub vegetation during the last glacial period. There may be both wind- and insect-pollinated species. Erica is mainly used as fuelwood in Madagascar, but local uses as tools and medicine have also been reported. Estimates suggest at least one-fifth of the species may be threatened, but formal assessments are lacking. Taxonomic revision of the group, coupled with phylogenomic, ecological and ethnobotanic studies, is an urgent priority.

Using comparative extinction risk analysis to prioritize the IUCN Red List reassessments of amphibians

Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land-use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species' range size and spatial configuration of the range, biological traits, climate change, and land-use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models' uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up-to-date IUCN RL.

Creating a more inclusive approach to wildlife trade management

Global wildlife trade involves a diverse array of species. Although sustainable trade underpins livelihoods for communities worldwide, unsustainable trade, whether legal or illegal, threatens thousands of species and can lead to extinctions. From plants and fungi to fish, amphibians, mammals, invertebrates, and reptiles, a diverse array of species across taxa are affected by trade. Attention to wildlife trade has increased in recent years, but its focus has largely remained on a narrow range of high-profile species, with taxa deemed less charismatic frequently overlooked, despite some having significant trade volumes and levels of threat to wild populations. These biases can hamper effective policy interventions, reduce awareness of wider threats from trade, and prevent conservation efforts from focusing on the most pressing issues. It is important to broaden the scope of research and policy discussions and create a more inclusive approach to trade management. The diversity of approaches to wildlife trade can be improved by expanding monitoring of trade to a wider variety of taxa; collecting fundamental ecological data to underpin assessments of trade sustainability; improving and codesigning conservation interventions with key stakeholders and trade actors; and developing appropriate strategies for managing the supply, trade, and demand in diverse wildlife products to ensure species and livelihoods are protected.

Effectiveness of South Africa's network of protected areas: Unassessed vascular plants predicted to be threatened using deep neural networks are all located in protected areas

Globally, we are in the midst of a biodiversity crisis and megadiverse countries become key targets for conservation. South Africa, the only country in the world hosting three biodiversity hotspots within its borders, harbours a tremendous diversity of at-risk species deserving to be protected. However, the lengthy risk assessment process and the lack of required data to complete assessments is a serious limitation to conservation since several species may slide into extinction while awaiting risk assessment. Here, we employed a deep neural network model integrating species climatic and geographic features to predict the conservation status of 116 unassessed plant species. Our analysis involved in total of 1072 plant species and 96,938 occurrence points. The best-performing model exhibits high accuracy, reaching up to 83.6% at the binary classification and 56.8% at the detailed classification. Our best-performing model at the binary classification predicts that 32% (25 species) and 8% (3 species) of Data Deficient and Not-Evaluated species respectively, are likely threatened, amounting to a proportion of 24.1% of unassessed species facing a risk of extinction. Interestingly, all unassessed species predicted to be threatened are in protected areas, revealing the effectiveness of South Africa's network of protected areas in conservation, although these likely threatened species are more abundant outside protected areas. Considering the limitation in assessing only species with available data, there remains a possibility of a higher proportion of unassessed species being imperilled.

Modelling the probability of meeting IUCN Red List criteria to support reassessments

Comparative extinction risk analysis-which predicts species extinction risk from correlation with traits or geographical characteristics-has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because existing models only predict a species' Red List category, without indicating which Red List criteria may be triggered. This prevents such approaches to be integrated into Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the world's birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g. range size, clutch size) and external drivers (e.g. change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species' Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at identifying threatened species compared to a criterion-blind model (higher sensitivity), but less good at identifying not threatened species (lower specificity). As expected, different covariates were important for predicting extinction risk under different criteria. Change in annual temperature was important for criteria related to population trends, while high forest dependency was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors. These species can then be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.

Indicators to monitor the status of the tree of life

Following the failure to fully achieve any of the 20 Aichi biodiversity targets, the future of biodiversity rests in the balance. The Convention on Biological Diversity's Kunming-Montreal Global Biodiversity Framework (GBF) presents the opportunity to preserve nature's contributions to people (NCPs) for current and future generations by conserving biodiversity and averting extinctions. There is a need to safeguard the tree of life-the unique and shared evolutionary history of life on Earth-to maintain the benefits it bestows into the future. Two indicators have been adopted within the GBF to monitor progress toward safeguarding the tree of life: the phylogenetic diversity (PD) indicator and the evolutionarily distinct and globally endangered (EDGE) index. We applied both to the world's mammals, birds, and cycads to show their utility at the global and national scale. The PD indicator can be used to monitor the overall conservation status of large parts of the evolutionary tree of life, a measure of biodiversity's capacity to maintain NCPs for future generations. The EDGE index is used to monitor the performance of efforts to conserve the most distinctive species. The risk to PD of birds, cycads, and mammals increased, and mammals exhibited the greatest relative increase in threatened PD over time. These trends appeared robust to the choice of extinction risk weighting. EDGE species had predominantly worsening extinction risk. A greater proportion of EDGE mammals (12%) had increased extinction risk compared with threatened mammals in general (7%). By strengthening commitments to safeguarding the tree of life, biodiversity loss can be reduced and thus nature's capacity to provide benefits to humanity now and in the future can be preserved.

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.

An integrated system to assess marine extinctions

More than 20 global marine extinctions and over 700 local extinctions have reportedly occurred during the past 500 years. However, available methods to determine how many of these species can be confidently declared true disappearances tend to be data-demanding, time-consuming, and not applicable to all taxonomic groups or scales of marine extinctions (global [G] and local [L]). We developed an integrated system to assess marine extinctions (ISAME) that can be applied to any taxonomic group at any geographic scale. We applied the ISAME method to 10 case studies to illustrate the possible ways in which the extinction status of marine species can be categorized as unverified, possibly extinct, or extinct. Of the 10 case studies we assessed, the ISAME method concludes that 6 should be categorized as unverified extinctions due to problems with species' identity and lack of reliable evidence supporting their disappearance (periwinkle-Littoraria flammea [G], houting-Coregonus oxyrinchus [G], long-spined urchin-Diadema antillarum [L], smalltooth sawfish-Pristis pectinata [L], and largetooth sawfish-P. pristis [L]). In contrast, ISAME classified the Guadalupe storm-petrel (Oceanodroma macrodactyla [G]) and the lost shark (Carcharhinus obsolerus [G]) as possibly extinct because the available evidence indicates that their extinction is plausible-while the largetooth sawfish [L] and Steller's sea cow (Hydrodamalis gigas [G]) were confirmed to be extinct. Determining whether a marine population or species is actually extinct or still extant is needed to guide conservation efforts and prevent further biodiversity losses.

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.

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.

Madagascar’s extraordinary biodiversity: Threats and opportunities

Madagascar’s unique biota is heavily affected by human activity and is under intense threat. Here, we review the current state of knowledge on the conservation status of Madagascar’s terrestrial and freshwater biodiversity by presenting data and analyses on documented and predicted species-level conservation statuses, the most prevalent and relevant threats, ex situ collections and programs, and the coverage and comprehensiveness of protected areas. The existing terrestrial protected area network in Madagascar covers 10.4% of its land area and includes at least part of the range of the majority of described native species of vertebrates with known distributions (97.1% of freshwater fishes, amphibians, reptiles, birds, and mammals combined) and plants (67.7%). The overall figures are higher for threatened species (97.7% of threatened vertebrates and 79.6% of threatened plants occurring within at least one protected area). International Union for Conservation of Nature (IUCN) Red List assessments and Bayesian neural network analyses for plants identify overexploitation of biological resources and unsustainable agriculture as the most prominent threats to biodiversity. We highlight five opportunities for action at multiple levels to ensure that conservation and ecological restoration objectives, programs, and activities take account of complex underlying and interacting factors and produce tangible benefits for the biodiversity and people of Madagascar.

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.