Browsing by Author "Cazalis, Victor"
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- Accelerating and standardising IUCN Red List assessments with sRedListPublication . Cazalis, Victor; Di Marco, Moreno; Zizka, Alexander; Butchart, Stuart H.M.; González-Suárez, Manuela; Böhm, Monika; Bachman, Steven P.; Hoffmann, Michael; Rosati, Ilaria; De Leo, Francesco; Jung, Martin; Benítez-López, Ana; Clausnitzer, Viola; Cardoso, Pedro; Brooks, Thomas M.; Mancini, Giordano; Lucas, Pablo M.; Young, Bruce E.; Akçakaya, H. Reşit; Schipper, Aafke M.; Hilton-Taylor, Craig; Pacifici, Michela; Meyer, Carsten; Santini, LucaThe IUCN Red List of Threatened Species underpins much decision-making in conservation and plays a key role in monitoring the status and trends of biodiversity. However, the shortage of funds and assessor capacity slows the uptake of novel data and techniques, hampering its currency, applicability, consistency and long-term viability. To help address this, we developed sRedList, a user-friendly online platform that assists Red List assessors through a step-by-step process to estimate key parameters in a standardised and reproducible fashion. Through the platform, assessors can swiftly generate outputs including species' range maps, lists of countries of occurrence, lower and upper bounds of area of occupancy, habitat preferences, trends in area of habitat, and levels of fragmentation. sRedList is compliant with the IUCN Red List guidelines and outputs are interoperable with the Species Information Service (SIS; the IUCN Red List database) in support of global, regional and national assessments and reassessments. sRedList can also help assessors prioritise species for reassessment. sRedList was released in October 2023, with a complete documentation package (including text documentation, ‘cheatsheets’, and 15 video tutorials), and will soon be highlighted in the official Red List online training course. sRedList will help to bridge the gap between extinction risk research and Red List assessment practice, increase the taxonomic coverage and consistency of assessments, and ensure the IUCN Red List is up-to-date to best support conservation policy and practice across the world.
- Modelling the probability of meeting IUCN Red List criteria to support reassessmentsPublication . Henry, Etienne G.; Santini, Luca; Butchart, Stuart H. M.; González‐Suárez, Manuela; Lucas, Pablo M.; Benítez‐López, Ana; Mancini, Giordano; Jung, Martin; Cardoso, Pedro; Zizka, Alexander; Meyer, Carsten; Akçakaya, H. Reşit; Berryman, Alex J.; Cazalis, Victor; Di Marco, MorenoComparative 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.