Fine-scale modelling of future alpine plant distribution in the Tyrolean Alps

Impacts of climate change on alpine vegetation are already emerging, especially the accelerating accumulation of colonisers from lower elevations on mountain summits. The underlying upward movement of the alpine flora seems to be in line with modelling studies that predict major habitat loss and widespread local to regional extinction of high-elevation species under climate warming. However, in the few cases where sufficient data allowed for quantifying the magnitude of upward shifts observed so far these shifts appeared much weaker than the warming assumed to drive them. One possible explanation is a pronounced inertia in distribution patterns resulting in the accumulation of a so-called extinction debt. An alternative explanation is the pronounced ruggedness and associated variation of micro-climates of the high-mountain terrain which might efficiently buffer species against climate warming. Other than spatial distributions, vital rates of plants will likely respond rapidly to a changing climate. Modelling the spatial distribution and temporal change of population growth rates (Demographic dispersal modelling DDM) is hence an alternative to conventional distribution modelling (SDM) which is less affected by potential disequilibria of species and environmental conditions. In the proposed project we aim to expand the dataset collected within the previous project MICROCLIM in order to fit and project both SDMs and DDMs across the alpine and nival landscape of the central part of the Tyrolean Alps. Model projections will be evaluated against GLORIA long-term monitoring data. Based on these model projections we aim to tackle the following questions: (1) How much of their currently suitable area will alpine plants lose under climate change until the end of the century according to SDMs fitted and projected on a 1m resolution? (2) Are these predicted losses significantly lower than those forecasted by models fit and projected at coarser spatial resolutions? (3) Do DDMs predict more pronounced losses than SDMs because they are better capable of de-tecting a likely current disequilibrium between climate and species distribution? (4) Do differences between SDM and DDM projections depend on species traits? (5) Where, within the study area, do both SDMs and DDMs predict major refugia of the high-mountain flora in warmer world?