Abstract: Objective　To investigate the current distribution and predict the future suitable habitats of Haemaphysalis longicornis in Nanjing City, so as to provide insights into control and early warning of ticks and management of tick⁃borne diseases in Nanjing City. Methods　The electronic map of Nanjing City was obtained from the National Platform for Common GeoSpatial Information Services. The distribution of H. longicornis and the longitude and latitude of distribution points from 2022 to 2024 were obtained from centers for disease control and prevention across each district in Nanjing City. Climatic and environmental variable data in Nanjing City were captured from the Worldclim database. Initially, 19 bioclimatic variables in this database were selected, including annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the warmest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The elevation and normalized difference vegetation index were obtained from Data Sharing Platform of the Center for Resources and Environmental Sciences, Chinese Academy of Sciences. Then, the distribution points of H. longicornis, elevation, vegetation index and 19 bioclimatic variables were loaded into the software MaxEnt 3.4.4 to evaluate and screen out the variables with a contribution rate of 1% and higher. ArcGIS 10.8.1 software was used to extract the elevation, vegetation index and 19 bioclimatic variables of the distribution points of H. longicornis for a correlation analysis. If the absolute value of the correlation coefficient was 0.8 and higher, the variable with the higher contribution was retained. The 2050 dataset of the BCCCSM2⁃MR atmospheric circulation model in the coupled model intercomparison project phase 6 (CMIP6) were obtained from the Worldclim database as climate data for 2050. Screened H. longicornis species data and environmental and climate data were loaded into the maximum entropy (MaxEnt) model with the software MaxEnt 3.4.4 for training and validation, and then, all data generated from the model were imported into the software ArcGIS 10.8.1 to generate raster data and yield the map pertaining to the distribution of H. longicornis risk in Nanjing City. The accuracy of the model was evaluated with a receiver operating characteristic (ROC) curve, and the predictive effect of the model was assessed with area under the ROC curve (AUC). The suitable habitats of H. longicornis were classified in Nanjing City with the software ArcGIS 10.8.1, and the areas of distribution of suitable habitats in various categories were recorded to create the map of current H. longicornis suitable habitats classification in Nanjing City. The climatic and geographic information data in 2050 were employed as future environmental and climatic factors, and current environmental and climatic factors and current H. longicornis distribution data were additionally used to predict the future suitable habitats of H. longicornis in Nanjing City. In addition, the contributions of environmental and climatic factors to distribution of suitable habitats of H. longicornis was evaluated with the Jackknife method in Nanjing City.  Results　A total of 10 environmental and climatic variables were screened for analysis of the suitability of H. longicornis in Nanjing City based on correlation analyses and contributions of the MaxEnt model, including annual mean temperature, precipitation of the warmest quarter, vegetation index, precipitation of the wettest month, temperature annual range, annual precipitation, mean temperature of the warmest quarter, elevation, mean temperature of the wettest quarter, and maximum temperature of the warmest month, and annual mean temperature (34.8%), precipitation of the warmest quarter (17.3%), vegetation index (13.1%), and precipitation of the wettest month (10.8%) contributed relatively highly to the distribution of suitable habitats of H. longicornis in Nanjing City. The mean AUC of the ROC curve was 0.810 ± 0.055 for 10 repeated modeling results of the MaxEnt model, indicating high predictive performance of the model. The potential distribution areas of H. longicornis were predicted to be mainly located in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District in Nanjing City with the MaxEnt model. Under current climatic conditions, the area of potential suitable habitats of H. longicornis was 4 182.42 km2 in Nanjing City, including 1 252.94 km2 highly suitable habitats, which accounted for 19.00% of the total area of Nanjing City. Under the climate scenario in 2050, the area of potential suitable habitats of H. longicornis was projected to increase to 5 467.58 km2 in Nanjing City, accounting for 82.95% of the total area of the city, and these habitats were mainly concentrated in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District. The areas of suitable habitats of H. longicornis at various categories were predicted to vary greatly in 2050, and the area of highly suitable habitats of H. longicornis was projected to increase to 2 378.82 km2, accounting for 36.08% of the total area of Nanjing City. Based on jackknife tests and contributions of environmental and climatic variables, 6 dominant environmental and climatic factors were screened, including annual mean temperature (34.8% contribution), precipitation of the warmest quarter (17.3% contribution), vegetation index (13.1% contribution), precipitation of the wettest month (10.8% contribution), temperature annual range (5.4% contribution), and mean temperature of the warmest quarter (5.0% contribution), with cumulative contributions of 86.4%. Conclusion　The distribution of H. longicornis is strongly associated with vegetation, temperature and precipitation in Nanjing City. Future climate change may lead to an expansion of the distribution area of H. longicornis in Nanjing City.

Key words: Haemaphysalis longicornis, Suitable habitat, Maximum entropy model, Nanjing City

摘要： 目的　分析南京市长角血蜱（Haemaphysalis longicornis）分布现状并预测其未来适生区范围，为该市蜱媒控制预警及蜱传疾病防治提供参考。方法　于国家地理信息公共服务平台获取南京市电子地图。于南京市各区疾病预防控制中心获取2022—2024年各区长角血蜱分布数据及分布点经纬度信息。于世界气候数据库获取该期间南京市气候环境变量数据，初步选取该数据库中全部19个生物气候变量，包括年平均气温、昼夜温差月均值、等温性、温度季节性变化、最暖月份最高气温、最暖月份最低气温、平均年温差、最湿季度平均温度、最干季度平均温度、最暖季度平均温度、最冷季度平均温度、年降水量、最湿月份降水量、最干月份降水量、季节性降水量、最湿季度降水量、最干季度降水量、最暖季度降水量和最冷季度降水量。于中国科学资源环境数据中心共享平台获取海拔、归一化植被指数。将长角血蜱分布点、海拔、植被指数和19个生物气候变量数据导入MaxEnt 3.4.4软件，评估并筛选出贡献率≥ 1%的变量。采用ArcGIS 10.8.1软件提取长角血蜱分布点海拔、植被指数和上述19个生物气候变量数据并进行相关性分析，当相关系数绝对值≥ 0.8时，保留两者中贡献率较高者。在世界气候数据库中，选取第六次国际耦合模式比较计划中的BCCCSM2⁃MR大气环流模型2050年数据集作为2050年气候数据。采用MaxEnt 3.4.4软件，将筛选所得长角血蜱物种数据与环境气候数据导入最大熵（maximum entropy，MaxEnt）模型进行训练和验证，将模型输出结果导入ArcGIS 10.8.1软件形成栅格数据，得到南京市长角血蜱风险分布图。采用受试者工作特征（receiver operating characteristic，ROC）曲线验证模型准确性，并以ROC曲线下面积（area under curve，AUC）评估预测效果。采用ArcGIS 10.8.1软件对南京市长角血蜱适生区进行等级划分，统计不同等级适生区分布面积，绘制当前南京市长角血蜱适生区等级图。以2050年气候与地理信息数据作为未来环境气候因子，结合当前环境气候因子及长角血蜱分布数据预测未来南京市长角血蜱适生区。采用刀切法评估各环境气候因子对南京市长角血蜱适生区分布的影响。结果 　结合相关性分析与MaxEnt模型贡献率，筛选出年平均气温、最暖季度降水量、植被指数、最湿月份降水量、平均年温差、年降水量、最暖季度平均温度、海拔、最湿季度平均温度和最暖月份最高气温10个环境气候变量用于南京市长角血蜱适生性分析，其中年平均气温、最暖季度降水量、植被指数和最湿月份降水量贡献率较高，分别为34.8%、17.3%、13.1%和10.8%。10次重复建立的MaxEnt模型ROC曲线AUC均值为0.810 ± 0.055，模型预测性能较好。MaxEnt模型预测结果显示，南京市长角血蜱潜在分布区主要位于六合区、浦口区、江宁区、溧水区和高淳区。当前气候条件下，南京市长角血蜱潜在适生区面积为4 182.42 km2；其中高适生区面积为1 252.94 km2，占南京市总面积的19.00%。在未来2050年气候情景下，南京市长角血蜱潜在适生区面积将增至5 467.58 km2，占全市总面积的82.95%；适生区主要集中于六合区、浦口区、江宁区、溧水区与高淳区；不同等级适生区面积较当前变化较大，其中高适生区面积增至2 378.82 km2，占南京市总面积的36.08%。综合刀切法检验和环境气候变量贡献率，筛选出年平均气温（贡献率34.8%）、最暖季度降水量（贡献率17.3%）、植被指数（贡献率13.1%）、最湿月份降水量（贡献率10.8%）、平均年温差（贡献率5.4%）、最暖季度平均温度（贡献率5.0%）6个主导环境气候因子，累计贡献率为86.4%。结论　南京市长角血蜱分布与植被、气温和降水量密切相关。未来气候变化可能导致南京市长角血蜱分布区域扩大。

关键词: 长角血蜱, 适生区, 最大熵模型, 南京市