Potential distributions of seven sympatric sclerophyllous oak species in Southwest China depend on climatic, non-climatic, and independent spatial drivers

• Key message An ensemble modelling approach was performed to predict the distributions of seven sympatric sclerophyllous oak species in the Hengduan Mountains of Southwest China. Spatial eigenvector filters revealed missing factors in addition to commonly used environmental variables, thus effectively improved predictive accuracy for the montane oak species. This study identified a richness center of sclerophyllous oaks, which provides a reference for proper conservation and utilization of oak resources. • Context As key species and important trees for construction- and fuel-wood, montane sclerophyllous oaks ( Quercus sect. Heterobalanus ) in the Hengduan Mountains of Southwest China are threatened by climate change, habitat fragmentation, and human activities. •Aims This study aims to simulate the potential distributions of seven sympatric sclerophyllous oak species with an emphasis on exploring the relative importance of climatic, non-climatic, and additional spatial factors. •Methods We performed an ensemble modelling approach of six ecological niche models in combination with spatial eigenvector filters to predict the potential distributions of seven oak species. •Results The results elucidated that temperature seasonality, followed by land use/cover and the human influence index were the most critical variables controlling oak species distributions. Regardless of the selected algorithm, the best performing models for most oaks combined climatic and non-climatic factors as well as additional spatial filters. •Conclusion It is necessary to strengthen the conservation of oak species at the junction of Sichuan and Yunnan Province where we found the richness center of the studied oaks. Our research provides essential insights for the rational conservation and management of sclerophyllous oak species, suggesting that spatial constraints might reflect limited ability of migration under future climate change.