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Species distribution models (SDMs) are subject to many sources of uncertainty, lim-iting their application in research and practice. One of their main limitations is the quality of the distributional data used to calibrate them, which directly influences the accuracy of model predictions. We propose a standardized methodology to cre-ate maps, describing the limitations of occurrence data for covering the distribution of a species. We develop a set of tools based on the general framework of Maps of Biogeographical Ignorance to describe the main sources of data-driven uncertainty: taxonomic stability, environmental similarity, geographical proximity and temporal decay of the underlying biodiversity data. The so-derived indicators of data-driven uncertainty account for inventory completeness, taxonomic quality, time since the surveys and geographical (and environmental) distance to localities with information. These indicators form the basis of ignorance maps, which can be used to visualize the reliability of SDM projections in geographical space, to estimate the uncertainty of these predictions and to identify target survey areas. To demonstrate the application of our approach, we use data on fourteen Iberian species of Scarabaeidae dung beetles. Data-driven uncertainty is widespread even for this well-surveyed group; more than 60% of the region has distributional uncertainty values higher than 0.6, and 30% higher than 0.7. Ignorance maps can be jointly evaluated with SDM predictions to generate spatially explicit maps of uncertainty, identifying where predictions are reli-able/unreliable. Neglecting such uncertainty can severely affect SDM effectiveness, as it can introduce biases and inaccuracies into the measured species–environment rela-tionships. These errors could result in incorrect theoretical or practical applications, including ill-advised conservation actions. We therefore advocate for the routine use of ignorance maps or similar techniques as supporting in

Knowledge on the relationships between species functional traits and environmental filters is key to understanding the mechanisms underlying the current patterns of biodiversity loss from a multi-taxa perspective. The aim of this study was to identify the main environmental factors driving the functional structure of a terrestrial vertebrate community (mammals, breeding birds, reptiles and amphibians) in a temperate mountain system (the Cantabrian Mountains; NW Spain). Based on the Spanish Inventory of Terrestrial Vertebrate Species, we selected three functional traits (feeding guild, habitat use type and daily activity) and defined, for each trait, a set of functional groups considering vertebrate species with common functional characteristics. The community functional structure was evaluated by means of two functional indexes indicative of functional redundancy (species richness within each functional group) and functional diversity. Ordinary least squares regression and conditional autoregressive models were applied to determine the response of community functional structure to environmental filters (climate, topography, land cover, physiological state of vegetation, landscape heterogeneity and human influence). The results revealed that both functional redundancy and diversity of terrestrial vertebrates were non-randomly distributed across space; rather, they were driven by environmental filters. Climate, topography and human influence were the best predictors of community functional structure. The influence of land cover, physiological state of vegetation and landscape heterogeneity varied among functional groups. The results of this study are useful to identify the general assembly rules of species functional traits and to illustrate the importance of environmental filters in determining functional structure of terrestrial vertebrate communities in mountain systems.

Recognizing the location of climatically stable areas in the future is subjected to uncertainties from ecological niche models, climatic models, variation in species ranges responses, and from the climatic variation through time. Here, we proposed an approach based on hierarchical ANOVA to reduce uncertainties and to identify climatically stable areas, working with Cerrado tree species as a model organism. Ecological niche models were generated for 18 Cerrado tree species and their potential distributions were projected into past and future. Analyses of the sources of uncertainties in ensembles hindcasts/forecasts revealed that the time component was the most important source of variation, whereas the climatic models had the smallest effect. The species responses to climate changes do not showed marked differences within each time period. By comparing past and future predictions, a single continuous climatically stable area was identified, which should be considered as a potential improvement for spatial prioritization for conservation.

Current climate and Pleistocene climatic changes are both known to be associated with geographical patterns of diversity. We assess their associations with the European Scarabaeinae dung beetles, a group with high dispersal ability and well-known adaptations to warm environments. By assessing spatial stationarity in climate variability since the last glacial maximum (LGM), we find that current scarab richness is related to the location of their limits of thermal tolerance during the LGM. These limits mark a strong change in their current species richness–environment relationships. Furthermore, northern scarab assemblages are nested and composed of a phylogenetically clustered subset of large-range sized generalist species, whereas southern ones are diverse and variable in composition. Our results show that species responses to current climate are limited by the evolution of assemblages that occupied relatively climatically stable areas during the Pleistocene, and by post-glacial dispersal in those that were strongly affected by glaciations.