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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 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.

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 exti

Aims A previous randomized study demonstrated that the subcutaneous implantable cardioverter defibrillator (S-ICD) was noninferior to transvenous ICD with respect to device-related complications and inappropriate shocks. However, that was performed prior to the widespread adoption of pulse generator implantation in the intermuscular (IM) space instead of the traditional subcutaneous (SC) pocket. The aim of this analysis was to compare survival from device-related complications and inappropriate shocks between patients who underwent S-ICD implantation with the generator positioned in an IM position in comparison with an SC pocket. Methods and results We analysed 1577 consecutive patients who had undergone S-ICD implantation from 2013 to 2021 and were followed up until December 2021. Subcutaneous patients (n = 290) were propensity matched with patients of the IM group (n = 290), and their outcomes were compared. : During a median follow-up of 28 months, device-related complications were reported in 28 (4.8%) patients and inappropriate shocks were reported in 37 (6.4%) patients. The risk of complication was lower in the matched IM group than in the SC group [hazard ratio 0.41, 95% confidence interval (CI) 0.17-0.99, P = 0.041], as well as the composite of complications and inappropriate shocks (hazard ratio 0.50, 95% CI 0.30-0.86, P = 0.013). The risk of appropriate shocks was similar between groups (hazard ratio 0.90, 95% CI 0.50-1.61, P = 0.721). There was no significant interaction between generator positioning and variables such as gender, age, body mass index, and ejection fraction. Conclusion Our data showed the superiority of the IM S-ICD generator positioning in reducing device-related complications and inappropriate shocks.

Despite being central to the implementation of conservation policies, the IUCN Red List of Threatened Species is hampered by the 14% of species classified as Data Deficient (DD), either because information to evaluate these species’ extinction risk was lacking when they were last assessed or because assessors did not appropriately account for uncertainty. With limited funds and time for reassessment, robust methods are needed to identify which DD species are more likely to be reclassified in one of the data sufficient Red List categories. Here we present a reproducible workflow to help Red List assessors prioritise reassessment of DD species, and tested it with 6,887 DD species of mammals, reptiles, amphibians, fishes, and Odonata (dragonflies and damselflies). Our workflow provides for each DD species: (i) the probability of being classified in a data sufficient category if reassessed today, (ii) the change in such probability since last assessment, and (iii) whether the species might qualify as threatened based on the recent rate of habitat loss. Combining these three elements, our workflow provides a priority list for reassessment of species more likely to be data sufficient, thus ultimately improving knowledge of poorly known species and the comprehensiveness and representativeness of the IUCN Red List.

14 december 2022, Contains fulltext : 287546.pdf (Publisher’s version ) (Open Access), Datasets for the paper: Uncovering the environmental drivers of intraspecific body size variation in terrestrial vertebrates. Includes dataset used for the analysis and Appendix S3. Bergmanns_bodysize.csv: This dataset contains body sizes for birds, mammals, amphibians, and reptiles, averaged within 10x10 km cells for each species. Body size is represented as mass (avg.mass) for birds and mammals and snout-vent length (avg.length) for amphibians and reptiles. Body size data were retrieved from the VertNet database (http://vertnet.org/resources/datatoolscode.html) and were limited to records of adult individuals, records after 1970, and records georeferenced with a precision of 10km or less . Environmental variables were also extracted for each cell: Mean (tavg; °C), minimum (tmin) and maximum (tmax) annual temperature and mean annual precipitation (prec; mm) were retrieved from WorldClim version 2.0 at a resolution of 5 arcminutes (http://worldclim.org; Fick & Hijmans, 2017). These represent average values from 1970-2000. Mean annual potential evapotranspiration (pet; mm/year) was retrieved from the ENVIREM database (Title & Bemmels, 2018; https://envirem.github.io/#downloads) at a 5 arcminute resolution. This represents the average from 1960-1990. Mean annual net primary productivity (npp; g/m2/day) and annual standard deviation of NPP (npp.sd) were retrieved from from the NASA Earth Observatory (ftp://neoftp.sci.gsfc.nasa.gov/geotiff.float/MOD17A2_M_PSN/) from 2000-2017 at a resolution of 6 arcminutes. Appendix S3. Latitudinal_range_species_records.csv: Dataset containing the minimum (lat.min), maximum (lat.max), and range of latitudes (lat.rng) represented for each species included in the analysis.

Radboud University, 03 mei 2023, Promotor : Huijbregts, M.A.J. Co-promotores : Santini, L., Tucker, A., Contains fulltext : 292309.pdf (Publisher’s version ) (Open Access), Macroecological research has grown rapidly over the last few decades, investing most of its efforts on describing patterns of biodiversity and human-induced threats using correlative- based approaches. Although correlative studies have provided important insights, they are unable to discern correlations from causal relationships. This limits our ability to generate reliable projections outside the spatio-temporal and taxonomic domains of the input data. In contrast, process-based models attempt to replicate the dynamics of a system, modeling directly its underlying mechanisms. As such, process-based methods have the potential to provide more reliable predictions under novel conditions. Despite recent advancements in ecological theory, increases in data availability and the growth of computational power, it remains a significant challenge to construct and further develop process-based ecosystem models that are widely applicable to different questions and ecological contexts. The main aim of this thesis was to develop and apply processed-based ecological models to better understand and improve their functioning, expand current ecological theory as well as increase the support of process-based modeling. To increase the accessibility and reproducibility of the Madingley model, I developed a user-friendly and transparent R interface called MadingleyR. I also assessed the applicability of General Ecosystem Models (GEMs), in particular the Madingley model, for interpreting ecological hypotheses as well as exploring conservation scenarios. In this context, I investigated the roles large-bodied mammals in regulating the trophic structure and maintaining ecosystem functioning. Considering the limitations that follow from the global-scale scope of the Madingley model, I developed and validated a new GEM, focusing on landscape-level simulations, though the integration of species identity, species-specific interactions and animal movement.

Halting the alarming rate of species extinction, driven primarily by habitat destruction, motivated the international community to adopt the Global Biodiversity Framework (2022) and its targets aimed at reversing habitat and species loss. Because of urgency and resource constraints, a key challenge is meeting targets effectively and efficiently. Here we conduct a global prioritization linking 70,492 unique population maps and life history characteristics for 861 threatened terrestrial mammal species. Incorporating individual population data to identify priority areas for conservation nearly doubled the likely long-term persistence of species for the same amount of land compared with a typical approach based on species distributions alone. We map and rank global mammal persistence priority areas and assess how well the current protected area (PA) system captures these important regions. Our results offer a clearer, quantifiable link between conservation actions and global extinction risk than previously possible at a global scale.

Halting the alarming rate of species extinction, driven primarily by habitat destruction, motivated the international community to adopt (2022) the Global Biodiversity Framework and it’s 23 targets aimed at reversing habitat and species loss. Due to urgency and resource constraints, a key challenge is meeting targets effectively and efficiently. Here we conduct a global prioritization linking 70,492 unique population maps and life history characteristics for 861 threatened terrestrial mammal species. Incorporating individual population data boosted viability for 84% more populations compared to using a more typical approach that uses species distributions alone, nearly doubling the security of long-term species persistence. We map and rank global mammal persistence priority areas and assess how well the current protected areas (PA) system captures these important regions. Our results will provide conservation actors a more direct and quantifiable linkage between conservation action and extinction risk than has previously been possible at a global scale.

The viability of populations can be quantified with several measures, such as the probability of extinction, the mean time to extinction, or the population size. While conservation management decisions can be based on these measures, it has not yet been explored systematically if different viability measures rank species and scenarios similarly and if one viability measure can be converted into another to compare studies. To address this challenge, we conducted a quantitative comparison of eight viability measures based on the simulated population dynamics of more than 4500 virtual species. We compared (a) the ranking of scenarios based on different viability measures, (b) assessed direct correlations between the measures, and (c) explored if parameters in the simulation models can alter the relationship between pairs of viability measures. We found that viability measures ranked species similarly. Despite this, direct correlations between the different measures were often weak and could not be generalized. This can be explained by the loss of information due to the aggregation of raw data into a single number, the effect of model parameters on the relationship between viability measures, and because distributions, such as the probability of extinction over time, cannot be ranked objectively. Similar scenario rankings by different viability measures show that the choice of the viability metric does in many cases not alter which population is regarded more viable or which management option is the best. However, the more two scenarios or populations differ, the more likely it becomes that different measures produce different rankings. We thus recommend that PVA studies publish raw simulation data, which not only describes all risks and opportunities to the reader but also facilitates meta-analyses of PVA studies.

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 exti

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.