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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. (C) 2019 The Authors. Published by Elsevier B.V.