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Protected areas (PAs) play a critical role in conserving biodiversity and maintaining viable populations of threatened species. Yet, as global change could reduce the future effectiveness of existing PAs in covering high species richness, updating the boundaries of existing PAs or creating new ones might become necessary to uphold conservation goals. Modelling tools are increasingly used by policymakers to support the spatial prioritization of biodiversity conservation, enabling the inclusion of scenarios of environmental changes to achieve specific targets. Here, using the Western Swiss Alps as a case study, we show how integrating species richness derived from species distribution model predictions for four taxonomic groups under present and future climate and land-use conditions into two conservation prioritization schemes can help optimize extant and future PAs. The first scheme, the "Priority Scores Method" identified priority areas for the expansion of the existing PA network. The second scheme, using the zonation software, allowed identifying priority conservation areas while incorporating global change scenarios and political costs. We found that existing mountain PAs are currently not situated in the most environmentally nor politically suitable locations when maximizing alpha diversity for the studied taxonomic groups and that current PAs could become even less optimum under the future climate and land-use change scenarios. This analysis has focused on general areas of high species richness or species of conservation concern and did not account for special habitats or functional groups that could have been used to create the existing network. We conclude that such an integrated framework could support more effective conservation planning and could be similarly applied to other landscapes or other biodiversity conservation indices.

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on‐ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision‐making contexts when used within a structured and transparent decision‐making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision‐making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes. ; AG's stay in Brisbane, Australia, was supported by the CSIRO McMaster Foundation. The three workshops (held on December 2011, April and May 2012) that led to this publication were organised with financial support and within the framework of the Australian Research Council Centre of Excellence for Environmental Decisions (CEED; http://www.ceed.edu.au) led by HPP. AG benefitted from insights from a project on applying SDMs to invasive management in Switzerland granted by the Swiss Federal Office of the Environment (FOEN) and the National Centre for Competence in Research (NCCR) 'Plant Survival' in Neuchâtel. LB benefitted from support from the Catalan Government (CARTOBIO and 2010‐BE‐272 projects) and the EU‐FP7 SCALES (#226852) to attend the workshops.