Abstract: Objective　To investigate the factors affecting the distribution of Pomacea and project the trends in the spread of suitable distribution areas of Pomacea in 2050 and 2070 in Dali Bai Autonomous Prefecture, so as to provide insights into Pomacea control in the prefecture. Methods　The longitudes and latitudes of Pomacea sampling sites were captured based on Pomacea field survey data in 12 cities (counties) of Dali Bai Autonomous Prefecture from 2023 to 2024. A total of 19 climatic factors (annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest month, mean temperature of the coldest month, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, mean temperature of the warmest quarter, and mean temperature of the coldest quarter) and representative concentration pathways (RCPs) were retrieved from the world climate database (www.worldclim.org). All climatic variables were employed to create a maximum entropy (MaxEnt) model. The predictive accuracy of the model was assessed with the area under the receiver operating characteristic (ROC) curve (AUC), and the contributions of these 19 climatic factors to the distribution of Pomacea were analyzed in Dali Bai Autonomous Prefecture using Jackknife test. In addition, the suitable distribution areas of Pomacea were predicted with the MaxEnt model in Dali Bai Autonomous Prefecture in 2024 and in 2050 and 2070 under RCP4.5. Results　Data pertaining to 91 Pomacea sampling sites were captured. ROC analysis revealed the MaxEnt model had an AUC value of 0.885 ± 0.088 for predicting the suitable distribution areas of Pomacea in Dali Bai Autonomous Prefecture. Of the 19 climatic factors, the maximum temperature of the warmest month had the highest contribution to the distribution of Pomacea in Dali Bai Autonomous Prefecture, followed by mean temperature of the driest quarter, mean temperature of the wettest quarter and minimum temperature of the coldest month. The suitable distribution area of Pomacea was predicted to be 14 555.69 m2 in Dali Bai Autonomous Prefecture in 2024, and would expand gradually to the southeastern part of the prefecture in the future due to climatic factors. The suitable distribution areas of Pomacea were projected to expand to 21 475.61 km² in 2050 and 25 782.52 km² in 2070 in Dali Bai Autonomous Prefecture, respectively. Conclusions　Temperature is an important contributor to the distribution of Pomacea in Dali Bai Autonomous Prefecture, and the suitable distribution area of Pomacea will gradually expand to the southeastern part of the prefecture in 2050 and 2070.

Key words: Pomacea, Distribution, Spread, Maximum entropy model, Suitable distribution area, Prediction, Dali Bai Autonomous Prefecture

摘要： 目的　了解云南省大理白族自治州福寿螺分布影响因素、预测2050年和2070年福寿螺适生区扩散趋势，为该地区福寿螺防控提供科学依据。方法　基于2023—2024年大理白族自治州12个市（县）福寿螺现场调查结果，获取福寿螺采样点经纬度信息。从全球气候数据网站（www.worldclim.org）获取年平均温度、平均日间温度范围、等温性、温度季节性、最暖月最高温度、最冷月最低温度、温度年度范围、最湿季平均温度、最干季平均温度、最暖季平均温度、最冷月平均温度、年降水量、最湿月降水量、最干月降水量、降水量季节性、最湿季降水量、最干季降水量、最暖季降水量、最冷季降水量等19种气候因子以及代表性浓度路径（representative concentration pathways，RCP）数据。将上述气候因子数据导入最大熵（maximum entropy，MaxEnt）模型软件构建模型，采用受试者工作特征（receiver operating characteristic，ROC）曲线下面积（area under curve，AUC）衡量模型预测的准确度，采用刀切法分析以上19种气候因子对大理白族自治州福寿螺分布的影响。采用MaxEnt模型预测2024年大理白族自治州福寿螺适生区分布，并借助RCP4.5情境下2050、2070年气候数据预测大理州福寿螺未来适生区分布。结果　共获取91个福寿螺采样点信息。ROC曲线分析结果显示，MaxEnt模型预测大理白族自治州福寿螺的AUC值为0.885 ± 0.088。19种气候因子中，最暖月最高温度对大理白族自治州福寿螺分布影响最大，其次是最干季平均温度、最湿季平均温度和最冷月最低温度。经预测，2024年大理白族自治州福寿螺适生区面积为14 555.69 m2；受气候因素影响，未来适生区将逐渐向该州东南部扩大，2050、2070年大理白族自治州福寿螺适生区面积将分别扩大至21 475.61 km2和25 782.52 km2。结论　温度是影响大理白族自治州福寿螺分布的重要因素，2050、2070年该州福寿螺适生区将逐渐向东南部扩大。

关键词: 福寿螺, 分布, 扩散, 最大熵模型, 适生区, 预测, 大理白族自治州