Getting in, extirpated out: How will mountain species and their contributions to people be reshuffled by climate change? Novel data and approaches for robust future spatial projections

Using stacked Nested-Species Distribution Models (N-SDMs) corrected for data biases and accounting for immigrant species, the thesis aims at generating unbiased and untruncated projections of mountain species distribution at fine resolution across any mountain range where the required data are available. This in turn would allow quantifying turnover in future species assemblage and ecosystems, and associated changes in Nature Contribution to People (NCPs), in far more robust and exhaustive ways. These unbiased/untruncated models and robust predictions/projections of species, ecosystems and NCP distributions could then be used to: i) identify data gaps (comparing expected (from models) and observed (reflecting the current state of sampling) species pool), ii) measure data (in)completeness to help direct future complementary sampling of biodiversity in mountain areas (e.g. through Global Mountain Biodiversity Assesment (GMBA) global assessment) towards identified data gaps, and iii) use the robust projections of species, communities and NCPs to document potential future trends in mountain biodiversity to be included in national and international biodiversity assessments.