Victor Cazalis’s research while affiliated with Leipzig University and other places

The 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 Web application GBIF Decision support 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.

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.

Variation in life histories influences demographic processes from adaptive changes to population declines leading to extinction. Among life history traits, generation length offers a critical feature to forecast species’ demographic trajectories such as population declines (widely used by the IUCN Red List of Threatened Species) and adaptability to environmental change over time. Therefore, estimates of generation length are crucial to monitor demographic stability or future change in highly threatened organisms, particularly ectothermic tetrapods (amphibians and reptiles) – which rank among the most threatened groups – but for which uncertainty in future impacts remains high. Despite its importance, generation length for amphibians and reptiles is largely missing. Here, we aimed to fill-in this gap by modeling generation lengths for amphibians, squamates and testudines as a function of species size, climate, life history, and phylogeny using generalized additive models and phylogenetic generalized least squares. We obtained estimates of generation lengths for 4,543 (52%) amphibians, 8,464 (72%) squamates and 118 (32%) testudines. Our models performed well for most families, for example Bufonidae in amphibians, Lacertidae and Colubridae in squamates and Geoemydidae in testudines, while we found high uncertainty around the prediction of a few families, notably Chamaeleonidae. Species’ body size and mean temperature were the main predictors of generation length in all groups. Although our estimates are not meant to substitute robust and validated measurements from field studies or natural history museums, they can help reduce existing biases in conservation assessments until field data will be comprehensively available.

The International Union for Conservation of Nature (IUCN) Red List is a central tool for extinction risk monitoring and influences global biodiversity policy and action. But, to be effective, it is crucial that it consistently accounts for each driver of extinction. Climate change is rapidly becoming a key extinction driver, but consideration of climate change information remains challenging for the IUCN. Several methods can be used to predict species’ future decline, but they often fail to provide estimates of the symptoms of endangerment used by IUCN. We devised a standardized method to measure climate change impact in terms of change in habitat quality to inform criterion A3 on future population reduction. Using terrestrial nonvolant tetrapods as a case study, we measured this impact as the difference between the current and the future species climatic niche, defined based on current and future bioclimatic variables under alternative model algorithms, dispersal scenarios, emission scenarios, and climate models. Our models identified 171 species (13% out of those analyzed) for which their current red‐list category could worsen under criterion A3 if they cannot disperse beyond their current range in the future. Categories for 14 species (1.5%) could worsen if maximum dispersal is possible. Although ours is a simulation exercise and not a formal red‐list assessment, our results suggest that considering climate change impacts may reduce misclassification and strengthen consistency and comprehensiveness of IUCN Red List assessments.

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.

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.

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 these models only predict a species′ Red List category, without indicating which Red List criteria may be triggered by which such approaches cannot easily be used in 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 predicting threatened species compared to a criterion–blind model (higher sensitivity), but less good at predicting not threatened species (lower specificity). As expected, different covariates were important for predicting threat status under different criteria, for example change in annual temperature was important to predict criteria related to population trends, while clutch size 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 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.

Assessing the extinction risk of species through the IUCN Red List is key to guiding conservation policies and reducing biodiversity loss. This process is resource-demanding, however, and requires a continuous update which becomes increasingly difficult as new species are added to the IUCN Red List. The use of automatic methods, such as comparative analyses to predict species extinction risk, can be an efficient alternative to maintaining up to date assessments. Using amphibians as a study group, we predict which species were more likely to change status, in order to suggest species that should be prioritized for reassessment. We used species traits, environmental variables, and proxies of climate and land-use change as predictors of the IUCN Red List category of species. We produced an ensemble prediction of IUCN Red List categories by combining four different model algorithms: Cumulative Link Models (CLM), phylogenetic Generalized Least Squares (PGLS), Random Forests (RF), Neural Networks (NN). By comparing IUCN Red List categories with the ensemble prediction, and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessments due to high prediction versus observation mismatch. We found that CLM and RF performed better than PGLS and NN, but there was not a clear best algorithm. The most important predicting variables across models were species range size, climate change, and land-use change. We propose ensemble modelling of extinction risk as a promising tool for prioritizing species for reassessment while accounting for inherent models' uncertainty.

The popular perception that humans are undergoing a global extinction of “experience of nature” (EoN) is poorly supported by empirical evidence. Here, we provide – to the best of our knowledge – the first global systematic review of trends in EoN, identifying only 18 studies that measured temporal trends in EoN. Of those 18 studies, several reported negative trends over time for both direct EoN (for example, in‐person visits to parklands) and vicarious EoN (specifically, the presence of nature in cultural products, such as movies or books), and all were biased toward North America, Western Europe, and Japan. As an initial proxy for understudied regions, we calculated past trends in three metrics of global EoN opportunities and show that, over the past decades, the locations where humans live have shifted away from the natural world and become more urban, while forest cover in cities has decreased. Overall, our results suggest that while EoN may be declining globally, existing evidence is insufficient to assess the magnitude and generality of this phenomenon.

Significance Human activities are causing biodiversity loss, but there is still strong debate on their effect on species richness. Here, I propose a unification of five trajectories of species richness response to increasing human pressure under the “replace then remove framework.” It consists in a first phase of assemblage transformation (with the replacement of “loser” by “winner” species), often followed by a second phase of steep decline in species richness (with the decline of many winner species) when human pressure exceeds a certain threshold. The empirical results presented in this study provide an outstanding illustration of assemblage transformations that may cause biotic homogenization, demonstrating how habitat specialist, endemic, sensitive, and threatened species are replaced by others with increasing human pressure.