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.

Chloroplast genome and nuclear loci data for 71 Medicago species.

We present a dataset containing nuclear and chloroplast sequences for 71 species in genus Medicago (Fabaceae), as well as for 8 species in genera Melilotus and Trigonella. Sequence data for a total of 130 samples was obtained with high-throughput sequencing of enriched genomic DNA libraries targeting 61 single-copy nuclear genes from across the Medicago truncatula genome. Chloroplast sequence reads were also generated, allowing for the recovery of chloroplast genome sequences for all 130 samples. A fully-resolved phylogenetic tree was inferred from the chloroplast dataset using maximum-likelihoood methods. More than 80% of accepted Medicago species are represented in this dataset, including three subspecies of Medicago sativa (alfalfa). These data can be further utilised for phylogenetic analyses in Medicago and related genera, but also for probe and primer design and plant breeding studies.