Targeting of intracellular oncoproteins with peptide-centric CARs.

The majority of oncogenic drivers are intracellular proteins, constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient for generating responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins essential for tumorigenesis. We focused on targeting the unmutated peptide QYNPIRTTF discovered on HLA-A*24:02, which is derived from the neuroblastoma-dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (PC-CARs) through a counter panning strategy using predicted potentially cross-reactive peptides. We further proposed that PC-CARs can recognize peptides on additional HLA allotypes when presenting a similar overall molecular surface. Informed by our computational modelling results, we show that PHOX2B PC-CARs also recognize QYNPIRTTF presented by HLA-A*23:01, the most common non-A2 allele in people with African ancestry. Finally, we demonstrate potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that PC-CARs have the potential to expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and allow targeting through additional HLA allotypes in a clinical setting.

Cross-HLA targeting of intracellular oncoproteins with peptide-centric CARs.

The majority of oncogenic drivers are intracellular proteins, thus constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient to generate responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins that are essential for tumourigenesis and focus on targeting the unmutated peptide QYNPIRTTF, discovered on HLA-A*24:02, which is derived from the neuroblastoma dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (CARs) using a counter-panning strategy with predicted potentially cross-reactive peptides. We further hypothesized that peptide-centric CARs could recognize peptides on additional HLA allotypes when presented in a similar manner. Informed by computational modelling, we showed that PHOX2B peptide-centric CARs also recognize QYNPIRTTF presented by HLA-A*23:01 and the highly divergent HLA-B*14:02. Finally, we demonstrated potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that peptide-centric CARs have the potential to vastly expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and widen the population of patients who would benefit from such therapy by breaking conventional HLA restriction.

Bending the curve of terrestrial biodiversity needs an integrated strategy.

Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it  provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether-and how-humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042-2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34-50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats-such as climate change-must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy.

Elevational homogenization of mountain parasitoids across six decades.

Elevational gradients are characterized by strong environmental changes within small geographical distances, providing important insights on the response of biological communities to climate change. Mountain biodiversity is particularly sensitive to climate change, given the limited capacity to colonize new areas and the competition from upshifting lowland species. Knowledge on the impact of climate change on mountain insect communities is patchy, but elevation is known to influence parasitic interactions which control insect communities and functions within ecosystems. We analyzed a European dataset of bristle flies, a parasitoid group which regulates insect herbivory in both managed and natural ecosystems. Our dataset spans six decades and multiple elevational bands, and we found marked elevational homogenization in the host specialization of bristle fly species through time. The proportion of specialized parasitoids has increased by ca. 70% at low elevations, from 17 to 29%, and has decreased by ca. 20% at high elevations, from 48 to 37%. As a result, the strong elevational gradient in bristle fly specialization observed in the 1960s has become much flatter over time. As climate warming is predicted to accelerate, the disappearance of specialized parasitoids from high elevations might become even faster. This parasitoid homogenization can reshape the ecological function of mountain insect communities, increasing the risk of herbivory outbreak at high elevations. Our results add to the mounting evidence that symbiotic species might be especially at risk from climate change: Monitoring the effects of these changes is urgently needed to define effective conservation strategies for mountain biodiversity.

Reptile research shows new avenues and old challenges for extinction risk modelling.

In a new PLOS Biology paper, de Oliveira Caetano and colleagues presented an innovative method to estimate extinction risk in reptile species worldwide. The method shows a promising avenue to support Red List assessment, alongside some well-known challenges.

Wilderness areas halve the extinction risk of terrestrial biodiversity.

Reducing the rate of global biodiversity loss is a major challenge facing humanity1, as the consequences of biological annihilation would be irreversible for humankind2-4. Although the ongoing degradation of ecosystems5,6 and the extinction of species that comprise them7,8 are now well-documented, little is known about the role that remaining wilderness areas have in mitigating the global biodiversity crisis. Here we model the persistence probability of biodiversity, combining habitat condition with spatial variation in species composition, to show that retaining these remaining wilderness areas is essential for the international conservation agenda. Wilderness areas act as a buffer against species loss, as the extinction risk for species within wilderness communities is-on average-less than half that of species in non-wilderness communities. Although all wilderness areas have an intrinsic conservation value9,10, we identify the areas on every continent that make the highest relative contribution to the persistence of biodiversity. Alarmingly, these areas-in which habitat loss would have a more-marked effect on biodiversity-are poorly protected. Given globally high rates of wilderness loss10, these areas urgently require targeted protection to ensure the long-term persistence of biodiversity, alongside efforts to protect and restore more-degraded environments.

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.

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.

Uso del análisis comparativo del riesgo de extinción para priorizar la reevaluación de los anfibios en la Lista Roja de la UICN Resumen El análisis del riesgo de extinción de una especie con base en la Lista Roja (LR) de la Unión Internacional para la Conservación de la Naturaleza (UICN) es clave para guiar las políticas de conservación y reducir la pérdida de la biodiversidad. Sin embargo, este proceso demanda recursos y requiere de actualizaciones continuas, lo que se complica conforme se añaden especies nuevas a la LR. Los métodos automáticos, como los análisis comparativos usados para predecir la categoría de la especie en la LR, pueden ser una alternativa eficiente para mantener actualizados los análisis. Usamos a los anfibios como grupo de estudio para predecir cuáles especies tienen mayor probabilidad de cambiar de categoría en la LR y que, por lo tanto, se debería priorizar su reevaluación. Usamos las características biológicas de la especie, las variables ambientales e indicadores climáticos y del cambio de uso de suelo como predictores de la categoría en la LR. Elaboramos una predicción de ensamble de la categoría en la LR de la UICN para cada especie mediante la combinación de cuatro algoritmos diferentes: modelos de vínculo acumulativo, menor número de cuadros filogenéticos generalizados, bosques aleatorios y redes neurales. Con la comparación entre las categorías de la LR y la predicción de ensamble y con considerar la incertidumbre entre los algoritmos identificamos especies que deberían ser prioridad para futuras reevaluaciones con base en el desfase entre los valores predichos y los observados. Las variables de predicción más importantes entre los modelos fueron el tamaño de la distribución de la especie y su configuración espacial, las características biológicas, el cambio climático y el cambio de uso de suelo. Comparamos nuestra propuesta de índice de priorización y los cambios predichos en la LR con las reevaluaciones independientes de la LR de la UICN y descubrimos un buen desempeño tanto para la priorización como para la direccionalidad predicha de los cambios en las categorías de la LR. El modelo de ensamble de la categoría de la LR esa una herramienta prometedora para priorizar la reevaluación de las especies a la vez que considera la incertidumbre del modelo. Esta estrategia puede generalizarse para aplicarse a todos los taxones de la LR de la UICN y a los análisis regionales y nacionales. También podría mejorar la asignación de los recursos humanos y económicos limitados disponibles para mantener actualizada la LR de la UICN.

A standard approach for including climate change responses in IUCN Red List assessments.

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.

Una estrategia estándar para incluir las respuestas al cambio climático en las evaluaciones de la Lista Roja de la UICN Resumen La Lista Roja de la Unión Internacional para la Conservación de la Naturaleza (UICN) es una herramienta central para el monitoreo del riesgo de extinción e influye sobre las acciones y políticas para la biodiversidad. Para que esta herramienta sea efectiva, es crucial que tenga en cuenta de manera regular cada factor de extinción. El cambio climático se está convirtiendo rápidamente en un factor de extinción importante, pero considerar información sobre este factor todavía es un reto para la UICN. Se pueden usar varios métodos para predecir la declinación de una especie en el futuro, pero generalmente fallan en proporcionar estimaciones de los síntomas del peligro usados por la UICN. Diseñamos un método estandarizado para medir el impacto del cambio climático en términos del cambio en la calidad del hábitat para informar el criterio A3 sobre la reducción futura de las poblaciones. Usamos a los tetrápodos terrestres no voladores como estudio de caso para medir este impacto como la diferencia entre el nicho climático actual y futuro de las especies, definido con base en las variables bioclimáticas actuales y futuras con algoritmos de modelos alternativos, escenarios de dispersión y emisión y modelos climáticos. Nuestros modelos identificaron 171 especies (13% de las especies analizadas) para las que su categoría actual en la lista roja podría empeorar bajo el criterio A3 si no logran dispersarse más allá de su distribución actual en el futuro. Las categorías para 14 especies (1.5%) podrían empeorar si es posible la dispersión máxima. Aunque realizamos una simulación y no una evaluación formal para listas rojas, nuestros resultados sugieren que considerar los impactos del cambio climático podría reducir la clasificación incorrecta y fortalecer la coherencia y exhaustividad de las evaluaciones de la Lista Roja de la UICN.

A protocol for harvesting biodiversity data from Facebook.

The expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity-a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority.

Un protocolo para recolectar datos sobre biodiversidad en Facebook Resumen El uso creciente de plataformas de ciencia comunitaria ha causado un incremento exponencial de los datos sobre biodiversidad en los repositorios mundiales. Sin embargo, el conocimiento sobre la distribución de las especies todavía está incompleto. Los datos sobre biodiversidad obtenidos de las redes sociales tienen el potencial para disminuir el vacío de conocimiento sobre la biodiversidad mundial. No obstante, no existe una guía práctica o un método estandarizado para recolectar dichos datos. Seguimos los protocolos de privacidad y protección de datos para diseñar un método estandarizado para extraer registros de la distribución de especies de grupos en Facebook que permiten el acceso a sus datos. El método consta de tres pasos: selección del grupo, extracción de datos y georreferenciación de la localidad registrada. También planteamos cómo estructurar las palabras clave, buscar fotografías de especies y georreferenciar las localidades de dichos registros. Además, resaltamos algunos retos que los usuarios pueden enfrentar al extraer los datos de distribución de Facebook y sugerimos algunas soluciones. Aplicamos nuestro marco de trabajo propuesto a un estudio de caso de la biodiversidad en Bangladesh, un país tropical megadiverso en el sureste de Asia. Reunimos casi 45,000 registros georreferenciados únicos para 967 especies y encontramos una media de 27 registros por especie. Casi el 12% de los datos de distribución correspondió a especies amenazadas, que representaban el 27% de todas las especies. También obtuvimos datos para 56 especies deficientes de datos en Bangladesh. Si los datos de las redes sociales se recolectan con cuidado, éstos pueden reducir de forma significativa el vacío de conocimiento para la biodiversidad mundial. Como consecuencia, es una prioridad para la investigación el desarrollo de una herramienta automatizada para extraer e interpretar los datos sobre biodiversidad de las redes sociales.

Using social media records to inform conservation planning.

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega-populated country, we examined the importance of social media records in conservation decision-making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF-only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000 km2 . Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.

Registros de las redes sociales para guiar la planeación de la conservación Resumen La ciencia ciudadana es importante para monitorear la biodiversidad e informar la conservación. Con el creciente uso de los teléfonos inteligentes, muchas personas comparten información de la biodiversidad en redes sociales, pero todavía no se usa ampliamente en la conservación. Analizamos la importancia de los registros de las redes sociales para las decisiones de conservación enfocados en Bangladesh, un país tropical megadiverso y mega poblado. Cotejamos los registros de distribución de especies de aves y mariposas en Facebook y Global Biodiversity Information Facility (GBIF), las agrupamos en datos sólo de GBIF o datos combinados de Facebook y GBIF e investigamos las diferencias en la identificación de las áreas de conservación críticas. La combinación de los datos de Facebook con los de GBIF mejoró la precisión de las evaluaciones de la planeación de la conservación sistemática al identificar otras áreas importantes de conservación en el noroeste, sureste y centro de Bangladesh, extendiendo así las áreas prioritarias de conservación en unos 4,000-10,000 km2 . Se requieren esfuerzos comunitarios para impulsar la implementación de los objetivos ambiciosos del Marco Global de Biodiversidad Kunming-Montreal, especialmente en países tropicales que carecen de datos confiables y actuales sobre la distribución de las especies. Destacamos que la planeación de la conservación puede mejorarse si se incluye información tomada de las redes sociales.

Scenarios of change in the realized climatic niche of mountain carnivores and ungulates.

Mountains are among the natural systems most affected by climate change, and mountain mammals are considered particularly imperiled, given their high degree of specialization to narrow tolerance bands of environmental conditions. Climate change mitigation policies, such as the Paris Agreement, are essential to stem climate change impacts on natural systems. But how significant is the Paris Agreement to the survival of mountain mammals? We investigated how alternative emission scenarios may determine change in the realized climatic niche of mountain carnivores and ungulates in 2050. We based our predictions of future change in species niches based on how species have responded to past environmental changes, focusing on the probabilities of niche shrink and niche stability. We found that achieving the Paris Agreement's commitments would substantially reduce climate instability for mountain species. Specifically, limiting global warming to below 1.5°C would reduce the probability of niche shrinkage by 4% compared with a high-emission scenario. Globally, carnivores showed greater niche shrinkage than ungulates, whereas ungulates were more likely to shift their niches (i.e., face a level of climate change that allows adaptation). Twenty-three species threatened by climate change according to the IUCN Red List had greater niche contraction than other species we analyzed (3% higher on average). We therefore argue that climate mitigation policies must be coupled with rapid species-specific conservation intervention and sustainable land-use policies to avoid high risk of loss of already vulnerable species.

Las montañas se encuentran entre los sistemas naturales más afectados por el cambio climático y se considera a los mamíferos montanos en un peligro particular debido a su alto nivel de especialización en condiciones ambientales dentro de un rango muy reducido de tolerancia. Las políticas de mitigación del cambio climático, como el Acuerdo de París, son esenciales para frenar el impacto del cambio climático sobre los sistemas naturales. ¿Pero cuán significativo es el Acuerdo de París para la supervivencia de los mamíferos montanos? Analizamos cómo los escenarios alternativos de emisiones pueden determinar el cambio en el nicho climático efectivo de los carnívoros y ungulados montanos en el 2050. Basamos nuestras predicciones del cambio en los nichos de las especies con base en cómo éstas han respondido a cambios ambientales anteriores, con un énfasis en las probabilidades de la reducción y la estabilidad del nicho. Descubrimos que lograr los compromisos del Acuerdo de París reduciría sustancialmente la inestabilidad climática para las especies montanas. En concreto, mantener el calentamiento global debajo de los 1.5°C reduciría la probabilidad de reducción del nicho en un 4% en comparación con un escenario de emisiones altas. A nivel mundial, los carnívoros mostraron una reducción de nicho mayor que el de los ungulados, mientras que los ungulados tuvieron mayor probabilidad de cambiar sus nichos (es decir, enfrentar un nivel de cambio climático que permite la adaptación). Veintitrés especies amenazadas por el cambio climático según la Lista Roja de la UICN tuvieron una mayor contracción del nicho que otras especies que analizamos (3% más alto, en promedio). Por esto argumentamos que las políticas de mitigación deben emparejarse con la intervención rápida de conservación específica por especie y las políticas de uso de suelo sustentable para evitar el riesgo elevado de pérdida de especies ya vulnerables. Escenarios de cambio en el nicho climático efectivo de carnívoros y ungulados montanos.

Opportunities and challenges for Common Agricultural Policy reform to support the European Green Deal.

The Common Agricultural Policy (CAP) is the European Union's main instrument for agricultural planning, with a new reform approved for 2023-2027. The CAP intends to align with the European Green Deal (EGD), a set of policy initiatives underpinning sustainable development and climate neutrality in the European Union (EU), but several flaws cast doubts about the compatibility of the objectives of these 2 policies. We reviewed recent literature on the potential of CAP environmental objectives for integration with the EGD: protection of biodiversity, climate change mitigation and adaptation, and sustainable management of natural resources. The CAP lacks appropriate planning measures, furthering instead risks to biodiversity and ecosystem services driven by landscape and biotic homogenization. Funding allocation mechanisms are not tailored to mitigate agricultural emissions, decreasing the efficiency of climate mitigation actions. The legislation subsidies farmers making extensive use of synthetic inputs without adequately supporting organic production, hindering the transition toward sustainable practices. We recommend proper control mechanisms be introduced in CAP Strategic Plans from each member state to ensure the EU is set on a sustainable production and consumption path. These include proportional assignment of funds to each CAP objective, quantitative targets to set goals and evidence-based interventions, and relevant indicators to facilitate effective monitoring of environmental performance. Both the CAP and the EGD should maintain ambitious environmental commitments in the face of crisis to avoid further degradation of the natural resources on which production systems stand.

Oportunidades y retos para la reforma a la Política Agrícola Común que respalden el Pacto Verde Europeo Resumen La Política Agrícola Común (PAC) es el principal instrumento de planificación agraria de la Unión Europea, con una nueva reforma aprobada para 2023-2027. La PAC pretende alinearse con el Pacto Verde Europeo (PVE), un conjunto de iniciativas políticas que apuntan al desarrollo sostenible y la neutralidad climática en la UE, aunque varias fallas han arrojado dudas sobre la compatibilidad de los objetivos de estas dos políticas. Revisamos la bibliografía reciente sobre el potencial de integración de los objetivos medioambientales de la PAC con el PVE en tres categorías: protección de la biodiversidad; mitigación del cambio climático y adaptación al mismo y, gestión sostenible de los recursos naturales. Encontramos que la PAC carece de medidas de planificación adecuadas, lo que agrava los riesgos para la biodiversidad y los servicios ambientales derivados de la homogeneización biótica y paisajística. Los mecanismos de asignación de fondos no están adaptados para mitigar las emisiones agrícolas, lo que disminuye la eficiencia de las acciones de mitigación del cambio climático. La legislación subsidia a los agricultores que hacen un uso extensivo de insumos sintéticos sin apoyar adecuadamente la producción ecológica, obstaculizando la transición hacia prácticas sostenibles. Recomendamos que se introduzcan mecanismos de control adecuados en los Planes Estratégicos de la PAC de cada Estado miembro para garantizar que la UE se encamina hacia una producción y un consumo sostenibles. Estos mecanismos incluyen la asignación proporcional de fondos a cada objetivo de la PAC, objetivos cuantitativos para fijar metas e intervenciones basadas en pruebas, e indicadores pertinentes para facilitar un seguimiento eficaz de los resultados medioambientales. Tanto la PAC como el PVE deben mantener compromisos medioambientales ambiciosos frente a la crisis para evitar una mayor degradación de los recursos naturales sobre los que se asientan los sistemas de producción.

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.

Priorización de la reevaluación de las especies con datos deficientes en la Lista Roja de la UICN Resumen No obstante que es fundamental para la implementación de políticas de conservación, la utilidad de la Lista Roja de Especies Amenazadas de la Unión Internacional para la Conservación de la Naturaleza (UICN) está limitada por el 14% de especies clasificadas con datos deficientes (DD) debido a que la información para evaluar el riesgo de extinción de estas especies no existía cuando fueron evaluadas la última vez o porque los evaluadores no consideraron la incertidumbre apropiadamente. Se requieren métodos robustos para identificar las especies DD con mayor probabilidad de ser reclasificadas en alguna de las categorías en la Lista Roja UICN con datos suficientes. Diseñamos un método reproducible para ayudar a que los evaluadores de la lista roja prioricen la reevaluación de especies DD y lo probamos con 6,887 especies DD de mamíferos, reptiles, anfibios, peces y Odonata (libélulas y caballitos del diablo). Para cada una de las especies DD en estos grupos, calculamos la probabilidad de ser clasificadas en una categoría con datos suficientes si fuera reevaluada hoy a partir de covariables que miden el conocimiento disponible (e.g., número de registros de ocurrencia o artículos publicados disponibles), sustitutos de conocimiento (e.g., extensión del rango de distribución) y características de la especie ((e.g., nocturnidad); calculamos el cambio en tal probabilidad desde la última reevaluación a partir del incremento en el conocimiento disponible (e.g., registros de ocurrencia nuevos); y determinamos si las especies podrían calificar como amenazadas con base en pérdidas de hábitat recientes a partir de mapas globales de cobertura de suelo recientes. Identificamos 1,907 especies con una probabilidad >0.5 de ser reclasificados en una categoría con datos suficientes; 624 especies cuya probabilidad aumentó en >0.25 desde la última evaluación, y 77 especies que podrían ser reclasificadas como casi en peligro con base en la pérdida de hábitat. Combinando estos 3 elementos, nuestros resultados proporcionaron una lista de especies probablemente con datos suficientes de tal modo que la exhaustividad y la representatividad de la Lista Roja de la UICN pueden ser mejoradas.

Reconciling global priorities for conserving biodiversity habitat.

Degradation and loss of natural habitat is the major driver of the current global biodiversity crisis. Most habitat conservation efforts to date have targeted small areas of highly threatened habitat, but emerging debate suggests that retaining large intact natural systems may be just as important. We reconcile these perspectives by integrating fine-resolution global data on habitat condition and species assemblage turnover to identify Earth's high-value biodiversity habitat. These are areas in better condition than most other locations predicted to have once supported a similar assemblage of species and are found within both intact regions and human-dominated landscapes. However, only 18.6% of this high-value habitat is currently protected globally. Averting permanent biodiversity loss requires clear, spatially explicit targets for retaining these unprotected high-value habitats.

Hotspots of human impact on threatened terrestrial vertebrates.

Conserving threatened species requires identifying where across their range they are being impacted by threats, yet this remains unresolved across most of Earth. Here, we present a global analysis of cumulative human impacts on threatened species by using a spatial framework that jointly considers the co-occurrence of eight threatening processes and the distribution of 5,457 terrestrial vertebrates. We show that impacts to species are widespread, occurring across 84% of Earth's surface, and identify hotspots of impacted species richness and coolspots of unimpacted species richness. Almost one-quarter of assessed species are impacted across >90% of their distribution, and approximately 7% are impacted across their entire range. These results foreshadow localised extirpations and potential extinctions without conservation action. The spatial framework developed here offers a tool for defining strategies to directly mitigate the threats driving species' declines, providing essential information for future national and global conservation agendas.

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates.

Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.

Conversión de la Categoría de Hábitat a Cobertura de Terreno para Mapear el Área de Hábitat de los Vertebrados Terrestres Resumen El área del hábitat (AOH) está definida como "el hábitat disponible para una especie, es decir, el hábitat dentro del área de distribución de la especie" y se calcula mediante la sustracción de las áreas de terreno inadecuado y la elevación del área de distribución. El Esquema de Clasificación de Hábitats de la Unión Internacional para la Conservación de la Naturaleza proporciona información sobre las asociaciones entre los hábitats de las especies y con frecuencia se utilizan las opiniones no validadas de expertos para cotejar el hábitat con los tipos de cobertura de terreno, lo que genera una fuente de incertidumbre en los mapas de AOH. Desarrollamos un método orientado por datos para convertir las categorías de hábitat que maneja la UICN en cobertura de terreno basado en los datos de localidad puntual de 6,986 especies de mamíferos terrestres, aves, anfibios y reptiles. Extrajimos la categoría de cobertura de terreno en cada localidad puntual y la cotejamos con la categoría o categorías de hábitat de UICN asignada a cada especie incidente en la localidad. Después modelamos cada categoría de cobertura de terreno como función del hábitat según la UICN usando modelos de regresión logística. Las proporciones de probabilidad resultantes fueron usadas para evaluar la solidez de la asociación entre cada categoría de hábitat y de cobertura de terreno. Después comparamos el desempeño de nuestro modelo orientado por datos con el desempeño de una tabla de conversión publicada basada en el conocimiento de expertos. Calculamos la asociación entre las categorías de hábitat y las de cobertura de terreno como una variable continua, pero para mapear el AOH como una presencia o ausencia binaria, fue necesario aplicar un umbral de asociación. Este umbral puede ser elegido por el usuario de acuerdo con el balance requerido entre los errores de omisión y comisión. Algunos hábitats (p. ej.: bosques y desiertos) fueron asignados a las categorías de cobertura de terreno con más confianza que otros (p. ej.: humedales y artificiales). El modelo de conversión orientado por los datos y el conocimiento de los expertos tuvieron un desempeño igual de eficiente, pero el modelo proporcionó una mayor estandarización, objetividad y repetitividad. Además, nuestra estrategia permitió una mayor flexibilidad en el uso de los resultados y de la incertidumbre para ser cuantificados. Nuestro modelo puede modificarse para análisis regionales y para diferentes grupos taxonómicos.

Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals.

Although habitat fragmentation is often assumed to be a primary driver of extinction, global patterns of fragmentation and its relationship to extinction risk have not been consistently quantified for any major animal taxon. We developed high-resolution habitat fragmentation models and used phylogenetic comparative methods to quantify the effects of habitat fragmentation on the world's terrestrial mammals, including 4,018 species across 26 taxonomic Orders. Results demonstrate that species with more fragmentation are at greater risk of extinction, even after accounting for the effects of key macroecological predictors, such as body size and geographic range size. Species with higher fragmentation had smaller ranges and a lower proportion of high-suitability habitat within their range, and most high-suitability habitat occurred outside of protected areas, further elevating extinction risk. Our models provide a quantitative evaluation of extinction risk assessments for species, allow for identification of emerging threats in species not classified as threatened, and provide maps of global hotspots of fragmentation for the world's terrestrial mammals. Quantification of habitat fragmentation will help guide threat assessment and strategic priorities for global mammal conservation.

Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling.

Nations have committed to ambitious conservation targets in response to accelerating rates of global biodiversity loss. Anticipating future impacts is essential to inform policy decisions for achieving these targets, but predictions need to be of sufficiently high spatial resolution to forecast the local effects of global change. As part of the intercomparison of biodiversity and ecosystem services models of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, we present a fine-resolution assessment of trends in the persistence of global plant biodiversity. We coupled generalized dissimilarity models, fitted to >52 million records of >254 thousand plant species, with the species-area relationship, to estimate the effect of land-use and climate change on global biodiversity persistence. We estimated that the number of plant species committed to extinction over the long term has increased by 60% globally between 1900 and 2015 (from ~10,000 to ~16,000). This number is projected to decrease slightly by 2050 under the most optimistic scenario of land-use change and to substantially increase (to ~18,000) under the most pessimistic scenario. This means that, in the absence of climate change, scenarios of sustainable socio-economic development can potentially bring extinction risk back to pre-2000 levels. Alarmingly, under all scenarios, the additional impact from climate change might largely surpass that of land-use change. In this case, the estimated number of species committed to extinction increases by 3.7-4.5 times compared to land-use-only projections. African regions (especially central and southern) are expected to suffer some of the highest impacts into the future, while biodiversity decline in Southeast Asia (which has previously been among the highest globally) is projected to slow down. Our results suggest that environmentally sustainable land-use planning alone might not be sufficient to prevent potentially dramatic biodiversity loss, unless a stabilization of climate to pre-industrial times is observed.