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Climate change can induce shifts in species ranges. Of special interest are range shifts in regions with a conflict of interest between land use and the conservation of threatened species. Here we focus on the 94 threatened terrestrial vertebrates occurring in the Madrid region (Central Spain) and model their distributions using data for the whole peninsular Spain to evaluate which vertebrate groups are likely to be more sensitive to climatic change. First, we generated predictive models to quantify the extent to which species distributions are explained by current climate. We then extrapolated the models temporally to predict the effects of two climate-change scenarios on species distributions. We also examined the impact on a recently proposed reserve relative to other interconnected zones with lower protection status but categorized as Areas of Community Importance by the European Union. The variation explained by climatic predictors was greater in ectotherms. The change in species composition differed between the proposed reserve and the other protected areas. Endothermic and ectothermic vertebrates had different patterns of changes in species composition but those of ectotherms matched with temperature departures predicted by climate change. Our results, together with the limited dispersal capacity of herptiles, suggest that trade-offs between different design criteria accounting for animal group differences are necessary for reserve selection. ; Financial support was provided to P.A. by the Comunidad de Madrid (GR/AMB/0920/2004) and by an I3P-PC2005L postdoctoral contract, by the Spanish Ministry of Science and Innovation (CGL2006-03000/BOS to M.Á.R., CGL2006-10196 to J.M.L and AP2005-0636 to M.Á.O.-T.), and by an FP7 Marie Curie Intra-European Fellowship (PHYLONICHE) from the European Commission to M.Á.O.-T. ; Peer reviewed

Here we present the code to generate MoBIs (Maps of Biogeographical Ignorance) for single species. The method is based on the calculation of different dimensions of biodiversity data (spatial, temporal and taxonomic). Specifically, in this method, we calculated completeness of each cell by generating accumulation curves with records of all individuals of a group. We also set taxonomy quality values for each identifier considering their experience with the group. For the temporal dimension, we adjust a decay curve to quantify temporal decay of information with time so that older records of the species are least informative and recent ones are the most informative. These components are summarized to generate an index varying from 0 to 1 (lower and higher ignorance, respectively) for each cell for a species. Finally, for cells without data we performed an interpolation, based on the BI calculated for neighbor cells. ; Funding – This work has been funded by the Brazilian CNPq PVE grant 314523/2014-6. GT was supported by CAPES REUNI doctorate fellowship and PDSE grant no. 11842121. RJL is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 854248.

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 information in SDM applications. ; This work has been funded by the Brazilian CNPq PVE grant 314523/2014-6 and the Brazilian National Inst. for Science and Technology in Ecology, Evolution and Biodiversity Conservation (INCT-EECBio), supported by MCTIC/CNPq (465610/2014-5) and the Fundação de Amparo à Pesquisa do Estado de Goiás (201810267000023). GT was supported by CAPES REUNI doctorate fellowship and PDSE grant no. 11842121. RJL is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 854248. ; Peer reviewed