Applying habitat and population-density models to land-cover time series to inform IUCN Red List assessments.

The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species' distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land-cover change, species-specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5-2.3% and 6.4-14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early-warning system to identify species potentially warranting changes in their extinction-risk category based on periodic updates of land-cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment.

Aplicación de Modelos de Hábitat y de Densidad Poblacional a Series de Tiempo de la Cobertura del Suelo para Informar las Valoraciones de la Lista Roja de la UICN Resumen Las categorías y los criterios de la Lista Roja de la UICN (Unión Internacional para la Conservación de la Naturaleza) son el marco de referencia utilizado con mayor frecuencia para valorar el riesgo de extinción relativo de las especies. Los criterios se basan en umbrales cuantitativos relacionados con el tamaño, las tendencias y la estructura de la distribución y las poblaciones de las especies. Sin embargo, los datos sobre estos parámetros son escasos e inciertos para muchas especies y para otras no se encuentran disponibles, lo puede resultar en una clasificación errónea o en que se las clasifique como una especie con deficiencia de datos. Hemos diseñado una estrategia que combina datos sobre el cambio en la cobertura del suelo, las preferencias de hábitat específicas por especie, la abundancia poblacional, y la distancia de dispersión para estimar los parámetros más importantes (extensión de la presencia, área máxima de ocupación, tamaño poblacional, y grado y tendencia de la fragmentación) y así predecir las categorías de la Lista Roja de la UICN para cada especie. Hemos aplicado nuestra estrategia a las aves no pelágicas y a los mamíferos terrestres de todo el mundo (∼15,000 especies). Las categorías pronosticadas fueron bastante consecuentes con las valoraciones publicadas por la Lista Roja de la UICN, aunque en general fueron más optimistas. Pronosticamos que el 4.2% de las especies (467 aves y 143 mamíferos) se encuentran más amenazadas que su valoración actual y el 20.2% de las especies con deficiencia de datos (10 aves y 114 mamíferos) se encuentran en riesgo de extinción. La incorporación del sub-criterio de fragmentación del hábitat redujo estas predicciones en un 1.5 - 2.3% y 6.4 - 14.9% (dependiendo de la definición cuantitativa de la fragmentación) para las especies amenazadas y las que tienen deficiencia de datos, respectivamente, lo que resalta la necesidad de mejorar la aplicación de este sub-criterio por parte de los asesores de la Lista Roja de la UICN. Nuestra estrategia complementa los métodos tradicionales de estimación de parámetros para las valoraciones de la Lista Roja. Además, proporciona un sistema inmediato de alerta temprana basado en actualizaciones periódicas de la información sobre la cobertura del suelo que permite identificar a las especies que, potencialmente, merezcan un cambio en su categoría de riesgo de extinción. Nuestro método está basado en suposiciones optimistas sobre la distribución y la abundancia de las especies, por lo tanto todas las especies que predecimos que tienen una mayor categoría de riesgo que la que reconoce la evaluación actual deberían ser priorizadas para su revaloración.

A High-Resolution Spatial Model to Predict Exposure to Pharmaceuticals in European Surface Waters: ePiE.

Environmental risk assessment of pharmaceuticals requires the determination of their environmental exposure concentrations. Existing exposure modeling approaches are often computationally demanding, require extensive data collection and processing efforts, have a limited spatial resolution, and have undergone limited evaluation against monitoring data. Here, we present ePiE (exposure to Pharmaceuticals in the Environment), a spatially explicit model calculating concentrations of active pharmaceutical ingredients (APIs) in surface waters across Europe at ∼1 km resolution. ePiE strikes a balance between generating data on exposure at high spatial resolution while having limited computational and data requirements. Comparison of model predictions with measured concentrations of a diverse set of 35 APIs in the river Ouse (UK) and Rhine basins (North West Europe), showed around 95% were within an order of magnitude. Improved predictions were obtained for the river Ouse basin (95% within a factor of 6; 55% within a factor of 2), where reliable consumption data were available and the monitoring study design was coherent with the model outputs. Application of ePiE in a prioritisation exercise for the Ouse basin identified metformin, gabapentin, and acetaminophen as priority when based on predicted exposure concentrations. After incorporation of toxic potency, this changed to desvenlafaxine, loratadine, and hydrocodone.

Global implications of crop-based bioenergy with carbon capture and storage for terrestrial vertebrate biodiversity.

Bioenergy with carbon capture and storage (BECCS) based on purpose-grown lignocellulosic crops can provide negative CO2 emissions to mitigate climate change, but its land requirements present a threat to biodiversity. Here, we analyse the implications of crop-based BECCS for global terrestrial vertebrate species richness, considering both the land-use change (LUC) required for BECCS and the climate change prevented by BECCS. LUC impacts are determined using global-equivalent, species-area relationship-based loss factors. We find that sequestering 0.5-5 Gtonne of CO2 per year with lignocellulosic crop-based BECCS would require hundreds of Mha of land, and commit tens of terrestrial vertebrate species to extinction. Species loss per unit of negative emissions decreases with: (i) longer lifetimes of BECCS systems, (ii) less overall deployment of crop-based BECCS and (iii) optimal land allocation, that is prioritizing locations with the lowest species loss per negative emission potential, rather than minimizing overall land use or prioritizing locations with the lowest biodiversity. The consequences of prevented climate change for biodiversity are based on existing climate response relationships. Our tentative comparison shows that for crop-based BECCS considered over 30 years, LUC impacts on vertebrate species richness may outweigh the positive effects of prevented climate change. Conversely, for BECCS considered over 80 years, the positive effects of climate change mitigation on biodiversity may outweigh the negative effects of LUC. However, both effects and their interaction are highly uncertain and require further understanding, along with the analysis of additional species groups and biodiversity metrics. We conclude that factoring in biodiversity means lignocellulosic crop-based BECCS should be used early to achieve the required mitigation over longer time periods, on optimal biomass cultivation locations, and most importantly, as little as possible where conversion of natural land is involved, looking instead to sustainably grown or residual biomass-based feedstocks and alternative strategies for carbon dioxide removal.

On the importance of predictor choice, modelling technique, and number of pseudo-absences for bioclimatic envelope model performance.

Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. Understanding how methodological choices influence these models is critical for a comprehensive evaluation of the estimated impacts. Here we systematically assess the performance of bioclimatic envelope models in relation to the selection of predictors, modeling technique, and pseudo-absences. We considered (a) five different predictor sets, (b) seven commonly used modeling techniques and an ensemble model, and (c) three sets of pseudo-absences (1,000 pseudo-absences, 10,000 pseudo-absences, and the same as the number of presences). For each combination of predictor set, modeling technique, and pseudo-absence set, we fitted bioclimatic envelope models for 300 species of mammals, amphibians, and freshwater fish, and evaluated the predictive performance of the models using the true skill statistic (TSS), based on a spatially independent test set as well as cross-validation. On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo-absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species-specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross-validation but had the largest decrease in model performance when transferred to a different spatial context, thus highlighting the need for spatially independent model evaluation. We recommend building bioclimatic envelope models according to an ensemble modeling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modeled species.