Distribution of potential suitable habitats for Haemaphysalis longicornis in Nanjing City based on the maximum entropy model
10.16250/j.32.1915.2025115
- VernacularTitle:基于MaxEnt模型的南京市长角血蜱 潜在适生区分布研究
- Author:
Pumin ZHOU
1
;
Jianjun XIA
2
;
Luyao SUN
2
;
Xuemin CHEN
3
;
Bingdong SONG
3
;
Shougang ZHANG
4
Author Information
1. School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
2. School of Public Health, Nanjing Medical University, China
3. Division of Disinfection and Vector Control, Nanjing Municipal Center for Disease Control and Prevention,Jiangsu Province, Nanjing, Jiangsu 210003, China
4. School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Division of Disinfection and Vector Control, Nanjing Municipal Center for Disease Control and Prevention,Jiangsu Province, Nanjing, Jiangsu 210003, China
- Publication Type:Journal Article
- Keywords:
Haemaphysalis longicornis;
Suitable habitat;
Maximum entropy model;
Nanjing City
- From:
Chinese Journal of Schistosomiasis Control
2026;38(1):44-53
- CountryChina
- Language:Chinese
-
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.
