Prediction of changes in suitable habitats for tea plants in China's four major tea-producing regions based on machine learning models.

Under the background of ongoing global climate warming, clarifying the spatiotemporal dynamics of suitable habitats for tea plants and their potential impact on forest ecosystems is essential for promoting sustainable tea industry development and ecological conservation. This study integrated machine learning and geospatial analysis, using 14 climate, topographic, and soil variables to construct five models-Random Forest (RF), MaxEnt, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and LightGBM. The best-performing RF model was selected to simulate current and future suitable habitats for tea plants across China's Southwest Tea Region, Jiangnan Tea Region, Jiangbei Tea Region, and South China Tea Region under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, SSP5-8.5) at a 1 km spatial resolution. Based on this, the predicted habitat maps were overlaid with current forest distribution data to assess the potential risk of habitat expansion encroaching on forest land.Results show that:(1) Currently, the area of high and moderate suitable habitats in the four major tea-producing regions reaches 3.4401 million km2, accounting for 86.84% of the total tea cultivation area, and is mainly distributed in warm, humid regions with favorable ecological conditions;(2) Under future warming scenarios, suitable habitats are expected to shift northward overall, with significant increases in suitability in the Jiangnan Tea Region and Jiangbei Tea Region, edge expansion in the Southwest Tea Region, and stable patterns in the South China Tea Region;(3) Habitat expansion may pose regionally differentiated pressures on forest land, with significant increases in overlap with forest areas in the Southwest Tea Region and Jiangbei Tea Region under high-emission scenarios, indicating rising ecological conflict risks.This study provides scientific support and spatial insights for optimizing tea cultivation zoning, coordinating agricultural and forestry land use, and implementing climate-adaptive management strategies.