Bioclimatic variables used for MaxEnt modeling.

The rapidly changing climate is impacting species globally at an unprecedented rate, including humans. Consequently, extensive research is being conducted on the impacts of climate change on indigenous and vulnerable species. However, landscape trees, which are cultivated and managed by humans, receive less attention despite their significant role in urban environments. Landscape tree also have specific climatic ranges and environmental requirements, making them susceptible to climate change. In this study, we predicted the future sustainability of three native landscape trees (Stewartia koreana, Betula ermanii, and Taxus cuspidata) using maximum entropy (MaxEnt) models under SSP2-4.5 and SSP5-8.5 climate scenarios. A time-series analysis of suitability was conducted, and the resulting maps were overlaid to classify regions of suitability. The findings indicate a general northward shift in climate suitability and a potential reduction in long-term suitable areas for all three species. Under the SSP2-4.5 scenario, potential suitable area for S. koreana increased, while those for B. ermanii, T. cuspidata decreased by the 2090s. Under the SSP5-8.5 scenario, suitable areas for S. koreana, B. ermanii, T. cuspidata decreased by 33.6%, 98.9%, and 90.1%, respectively. The climate suitability classification (“Sustainable suitability”, “Risk”, “Inflow”, “Lost”, and “Variable” regions) effectively identified areas of sustainability and risk, as well as regions requiring management. A notable decline in “Sustainable suitability” regions, which remained suitable from the present to the 2090s, was observed under the SSP5-8.5 scenario relative to SSP2-4.5. The methods utilized in this study offer valuable insights for future landscape planning and conservation. This research underscores the need for adaptive strategies to mitigate potential economic and ecological impacts of climate change by utilizing species distribution models for sustainable landscape planning and tree conservation.