Predicting the potential distribution areas of Leptotrombidium rubellum under current and future climate change

Qunzheng Mu^1,2Fengfeng Li³Wenyu Li^2,4Wang Xiaoxia¹Mingyuan Tang¹Kehan Chen¹Yihao Jiang¹Jingqi Liu¹Shirong Zhang¹Qiyong Liu²Chuan Wang^1*

• ¹Sichuan University, Chengdu, China
• ²Chinese Center for Disease Control and Prevention, Beijing, China
• ³Shandong Second Medical University, Weifang, China
• ⁴Ningxia Center for Diseases Prevention and Control, Yinchuan, China

Background: Leptotrombidium rubellum (L. rubellum), a confirmed vector of scrub typhus, was historically restricted to southeastern coastal China but has recently been detected in southwestern regions. Species distribution modeling was applied to predict its current and future potential distribution areas under multiple climate scenarios, identify high-priority surveillance areas, and determine key environmental drivers. The results may facilitate a transition from passive to proactive vector monitoring.: Fifty-seven potential influencing factors were evaluated. The maximum entropy (MaxEnt) model projected potential distribution areas for near current and future climate scenarios. Occurrence records were extracted from published literature. The selection of environmental variables was conducted using a multi-stage analytical approach, consisting of contribution rate assessment, jackknife tests, and correlation analyses. Model parameters were optimized via feature class and regularization multiplier adjustments. Results: The MaxEnt model demonstrated high predictive accuracy (AUC = 0.997) with minimal training omission error. July precipitation (prec7) and elevation (elev) were identified as the primary environmental determinants. Projections indicate near current suitable areas are concentrated in southern China, with potential northward expansion under future climate scenarios. Conclusion: L. rubellum exhibits broad distribution areas across China, with climate change likely driving suitable areas expansion. Enhanced surveillance in currently suitable and future at-risk regions is critical to mitigate invasion risks.