1. Comparative analyses are used to address the key question of what makes a species more prone to extinction by exploring the links between vulnerability and intrinsic species' traits and/or extrinsic factors. This approach requires comprehensive species data but information is rarely available for all species of interest. As a result comparative analyses often rely on subsets of relatively few species that are assumed to be representative samples of the overall studied group. 2. Our study challenges this assumption and quantifies the taxonomic, spatial, and data type biases associated with the quantity of data available for 5415 mammalian species using the freely available life-history database PanTHERIA. 3. Moreover, we explore how existing biases influence results of comparative analyses of extinction risk by using subsets of data that attempt to correct for detected biases. In particular, we focus on links between four species' traits commonly linked to vulnerability (distribution range area, adult body mass, population density and gestation length) and conduct univariate and multivariate analyses to understand how biases affect model predictions. 4. Our results show important biases in data availability with c.22% of mammals completely lacking data. Missing data, which appear to be not missing at random, occur frequently in all traits (14-99% of cases missing). Data availability is explained by intrinsic traits, with larger mammals occupying bigger range areas being the best studied. Importantly, we find that existing biases affect the results of comparative analyses by overestimating the risk of extinction and changing which traits are identified as important predictors. 5. Our results raise concerns over our ability to draw general conclusions regarding what makes a species more prone to extinction. Missing data represent a prevalent problem in comparative analyses, and unfortunately, because data are not missing at random, conventional approaches to fill data gaps, are not valid or present important challenges. These results show the importance of making appropriate inferences from comparative analyses by focusing on the subset of species for which data are available. Ultimately, addressing the data bias problem requires greater investment in data collection and dissemination, as well as the development of methodological approaches to effectively correct existing biases.

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Zoogeographical regions, or zooregions, are areas of the Earth defined by species pools that reflect ecological, historical and evolutionary processes acting over millions of years. Consequently, researchers have assumed that zooregions are robust and unlikely to change on a human timescale. However, the increasing number of human‐mediated introductions and extinctions can challenge this assumption. By delineating zooregions with a network‐based algorithm, here we show that introductions and extinctions are altering the zooregions we know today. Introductions are homogenising the Eurasian and African mammal zooregions and also triggering less intuitive effects in birds and amphibians, such as dividing and redefining zooregions representing the Old and New World. Furthermore, these Old and New World amphibian zooregions are no longer detected when considering introductions plus extinctions of the most threatened species. Our findings highlight the profound and far‐reaching impact of human activity and call for identifying and protecting the uniqueness of biotic assemblages.

Despite severe population declines and an overall range contraction, some populations of large carnivores have managed to survive in human-modified landscapes. From a conservation perspective, it is important to identify the factors allowing for this coexistence, including the relevant habitat characteristics associated with the presence of large carnivores. We evaluated the role of several environmental factors describing habitat quality for wolves Canis lupus in the humanised Iberian Peninsula, which currently holds an important wolf population at European level. We used maximum entropy and generalized linear model approaches in a nestedscale design to identify the environmental factors that are related to wolf presence at three spatial scales and resolutions: (1) distribution range: wolf presence on a 10 9 10 km grid resolution, (2) wolf habitat use: wolf occurrence on a 2 9 2 km grid and (3) dens/rendezvous sites: breeding locations on a 1 9 1 km grid. Refuge availability, as defined by topography, seemed to be the key factor determining wolf presence at the multiple scales analysed. As a result, wolf populations may coexist with humans in modified landscapes when the topography is complex. We found that a significant amount of favourable habitat is not currently occupied, suggesting that the availability of suitable habitat is not the limiting factor for wolves in the Iberian Peninsula. Habitat suitability outside the current range indicates that other factors, such as direct persecution and other sources of anthropogenic mortality, may be hampering its expansion. We suggest that priorities for conservation should follow two general lines: (1) protect good quality habitat within the current range; and (2) allow dispersal to unoccupied areas of good quality habitat by reducing human-induced mortality rates. Finally, we still need to improve our understanding of how wolves coexist with humans in modified landscapes at fine spatiotemporal scales, including its relationship with infrastructures, land uses and direct human presence.