Spatial Pattern and Environmental Driving Factors of Treeline Elevations in Yulong Snow Mountain, China

Abstract

The southwestern region of China is a global biodiversity hotspot. Understanding the environmental mechanisms behind treeline formation in high-altitude areas is crucial for predicting ecosystem changes, such as the upward movement of the treeline due to climate warming and the disappearance of high-altitude rocky beach and shrub ecosystems. Globally, observations show that growing seasonal temperatures at treelines are typically 6–7 °C, but trees do not always reach the predicted elevations. Spatial heterogeneity exists in the deviation (Dtreeline) between actual treeline elevation and the thermal treeline; however, the main driving factors for Dtreeline in many areas remain unclear. This study uses Yulong Snow Mountain as an example, employing machine learning methods like Support Vector Machine (SVM) to precisely identify actual treeline elevation and Extreme Gradient Boosting Tree (XGBoost) to explore the main environmental factors driving the spatial heterogeneity of Dtreeline. Our research found that (1) more than half of the treelines deviated from the thermal treeline, with the average elevation of the thermal treeline (3924 ± 391 m) being about 56 m higher than the actual treeline (3863 ± 223 m); (2) Dtreeline has a complex relationship with environmental factors. In addition to being highly correlated with temperature, precipitation and wind speed also significantly influence the treeline in this region; and (3) the influence of individual variables such as precipitation and wind speed on the spatial variation of Dtreeline is limited, often nonlinear, and involves threshold effects. This knowledge is essential for developing comprehensive protection strategies for Yunnan’s high-altitude ecological systems in response to climate warming. Furthermore, it plays a significant role in understanding the changes in biological communities and the response of high-altitude areas to climate change.