关键词: 鹅绒藤, 气候变化, MaxEnt模型, 环境因子, 适生区预测

Abstract: To identify the dominant environmental factors affecting the distribution of Cynanchum chinense R.Br.and their ecological thresholds，and to quantify its potential suitable areas at different periods，the MaxEnt model and ArcGIS spatial analysis technology were utilized. By integrating information on 301 distribution sites of Cynanchum chinense R.Br.in China and bioclimatic data，predictive analysis was conducted on its potential suitable areas during two past periods［the Last Glacial Maximum（about 22 000 years ago）and the Mid‑Holocene（about 6 000 years ago）］，the current period（1970—2000），and three future periods［2030s（2021—2040），2050s（2041—2060），and 2070s （2061—2080）］. The results indicated that the area under the curve（AUC）values of the MaxEnt model for each period were all above 0.9，indicating reliable prediction results.The distribution of Cynanchum chinense R.Br.was primarily influenced by annual average temperature，temperature seasonality coefficient，precipitation seasonality coefficient，and precipitation in the wettest month. Under current period，the total suitable area for Cynanchum chinense R.Br.reached 193. 376 75×10^⁴ km^²，with a high‑suitability area of 69. 015 63×10^⁴ km^².Compared to the current period，the suitable area of Cynanchum chinense R.Br.expanded significantly during the Last Glacial Maximum and the Mid‑Holocene. Among the three future periods，the suitable habitat area of Cynanchum chinense R.Br.was the largest in 2070s under the SSP370（regional competitive path） climate change scenario and the smallest in 2030s under the SSP126（sustainable path）climate change scenario，but both were larger than the current period suitable habitat area. In conclusion，Cynanchum chinense R.Br.showed an expansion trend in the two past periods，and the three future periods under different climate scenarios，reflecting the phased impact of climate change，especially temperature change caused by the carbon cycle，on species distribution.

Key words: Cynanchum chinense R.Br., Climate change, MaxEnt model, Environmental factor, Habitat suitability prediction