- SDMs, which rely on statistical correlations between occurrence records and environmental variables to make spatially explicit predictions, are commonly used to project climate change impacts on mountain ecosystems . - However, they are associated with some limitations that can limit their utility in deriving reliable predictions of future mountain vegetation distributions and, thus, ability to provide a sound basis for mountain nature conservation and climate change adaptation. - In particular, they only indicate the potential future species distributions based on static relationships between species and predictors in calibration data; in reality, vegetation dynamics will be heavily modulated by phenomena that are commonly overlooked by such models as changing species interactions and competition due to variance in response rates among different species, dispersal limitations and demographic processes . - In addition, SDMs are often limited by data availability and therefore tend to omit several environmental factors known to be important for plants, such as soil formation processes, disturbances  and microclimatic conditions . - More complex dynamic and process-based models are available but still rarely represent all potentially influential vegetation co-variates; applying both model types in conjunction holds potential . :unselected: :unselected: :unselected: :unselected:
IPCC
Climate Change 2022: Impacts, Adaptation and Vulnerability
IPCC Report