Table 1_Predicting the potential distribution areas of Leptotrombidium rubellum under current and future climate change.xlsx

BackgroundLeptotrombidium 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.
MethodsFifty-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.
ResultsThe 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.
ConclusionL. 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.

背景：红纤恙螨（Leptotrombidium rubellum, L. rubellum）是已确认的恙虫病传播媒介，历史上仅局限于中国东南沿海地区，但近年在西南地区已有检出。本研究采用物种分布建模（Species Distribution Modeling, SDM）方法，对该螨在多气候情景下的当前及未来潜在分布区进行预测，识别高优先级监测区域，并明确其关键环境驱动因子。研究结果可推动媒介监测从被动应对转向主动防控。

方法：本研究共评估了57项潜在影响因子。采用最大熵（MaxEnt）模型，基于近当前气候情景与未来气候情景预测潜在分布区。物种发生记录提取自已发表文献。环境变量的筛选采用多阶段分析流程，涵盖贡献度评估、刀切法检验与相关性分析。通过调整特征类与正则化乘数对模型参数进行优化。

结果：MaxEnt模型展现出优异的预测精度（受试者工作特征曲线下面积（Area Under Curve, AUC）=0.997），训练遗漏误差极小。7月降水量（prec7）与海拔（elev）被确定为主导该螨分布的关键环境因子。预测结果显示，当前适宜生境集中分布于中国南方，在未来气候情景下存在向北扩张的潜力。

结论：红纤恙螨在中国的潜在分布范围广泛，气候变化或推动其适宜生境进一步扩张。加强对当前适宜区域及未来潜在风险区域的监测，对降低其入侵风险至关重要。