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Climate change is gaining attention as a major threat to biodiversity. It is expected to further expand the risk of plant invasion through ecosystem disturbance. Particularly, island ecosystems are under pressure, and climate change may threaten forest-dependent species. However, scientific and societal unknowns make it di ffi cult to predict how climate change and biological invasions will affect species interactions and ecosystem processes. The purpose of this study was to identify possible limitations when making species distribution model projections based on predicted climate change. We aimed to know if climatic variables alone were good predictors of habitat suitability, ensuring reliable projections. In particular, we compared the performance of generalized linear models, generalized additive models, and a selection of machine learning techniques (BIOMOD 2) when modelling the distribution of forest species in the Azores, according to the climatic changes predicted to 2100. Some limitations seem to exist when modelling the effect of climate change on species distributions, since the best models also included topographic variables, making modelling based on climate alone less reliable, with model fit varying among modelling approaches, and random forest often providing the best results. Our results emphasize the adoption of a careful study design and algorithm selection process. The uncertainties associated with climate change effect on plant communities as a whole, including their indigenous and invasive components, highlight a pressing need for integrated modelling, monitoring, and experimental work to better realize the consequences of climate change, in order to ensure the resilience of forest ecosystems in a changing world.