Urbanisation exposes wildlife to new challenging conditions and environmental pressures. Some mammalian species have adapted to these novel environments, but it remains unclear which characteristics allow them to persist. To address this question, we identified 190 mammals regularly recorded in urban settlements worldwide, and used phylogenetic path analysis to test hypotheses regarding which behavioural, ecological and life history traits favour adaptation to urban environments for different mammalian groups. Our results show that all urban mammals produce larger litters; whereas other traits such as body size, behavioural plasticity and diet diversity were important for some but not all taxonomic groups. This variation highlights the idiosyncrasies of the urban adaptation process and likely reflects the diversity of ecological niches and roles mammals can play. Our study contributes towards a better understanding of mammal association to humans, which will ultimately allow the design of wildlife‐friendly urban environments and contribute to mitigate human‐wildlife conflicts.

Among plants and vertebrates, extinction risk is universally higher for the largest species with slower paces of life.

Roads represent a threat to biodiversity, primarily through increased mortality from collisions with vehicles. Although estimating roadkill rates is an important first step, how roads affect long‐term population persistence must also be assessed. We developed a trait‐based model to predict roadkill rates for terrestrial bird and mammalian species in Europe and used a generalized population model to estimate their long‐term vulnerability to road mortality. We found that ~194 million birds and ~29 million mammals may be killed each year on European roads. The species that were predicted to experience the highest mortality rates due to roads were not necessarily the same as those whose long‐term persistence was most vulnerable to road mortality. When evaluating which species or areas could be most affected by road development projects, failure to consider how roadkill affects populations may result in misidentifying appropriate targets for mitigation.

Aim: Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, whereas trait data are often not missing at random (e.g., more data for bigger species). Here, we evaluate the performance of approaches for handling missing values when considering biased datasets.Location: Any.Time period: Any.Major taxa studied: Any.Methods: We simulated continuous traits and separate response variables to test the performance of nine imputation methods and complete‐case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating the error in imputed trait values (deviation from the true value) and inferred trait–response relationships (deviation from the true relationship between a trait and response).Results: Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response–trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete‐case analysis frequently outperforming Mice imputation and, to a lesser degree, BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model.Main conclusions: Imputation can handle missing data effectively in some conditions but is not always the best solution. None of the methods we tested could deal effectively with severe biases, which can be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimize errors.