Predicting the Global Potential Distribution of Tow Major Vectors of Scrub Typhus Under Future Climate Scenarios

Predicting the Global Potential Distribution of Tow Major Vectors of Scrub Typhus Under Future Climate Scenarios | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting the Global Potential Distribution of Tow Major Vectors of Scrub Typhus Under Future Climate Scenarios Xinning Hao, Lianfang Feng, Zihang Wang, Haoqiang Ji, Shengping Dou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7592864/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Parasitology Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background Scrub typhus is a mite-borne disease caused by Orientia tsutsugamushi (Ot), primarily transmitted by the chigger mites Leptotrombidium deliense ( L. deliense ) and Leptotrombidium scutellare ( L. scutellare ). To effectively control the transmission of this disease, it is essential to clarify the spatial distribution of suitable habitats for its vector species. This research focuses on estimating the global potential distribution of Leptotrombidium deliense and Leptotrombidium scutellare under both current and projected future climate conditions. Additionally, the study aims to evaluate the influence of key climatic variables on the geographical patterns of these two vectors. Methods Data on species distribution were obtained from the Global Biodiversity Information Facility (GBIF) and from publicly published literature in databases such as Web of Science and PubMed. The environmental variables were downloaded from WorldClim Global Climate Database. The Maximum Entropy Model was used to evaluate the contribution of elevation, slope, aspect and nineteen bioclimatic variables to vector survival, as well as to predict the suitable area for the vectors. Results It indicated that L. deliense predominantly occurs in southern China, India, Australia, and Southeast Asian regions. In contrast, L. scutellare is largely restricted to southern and eastern coastal areas of China. L. deliense displays greater adaptability to tropical zones, whereas L. scutellare shows higher survival potential in temperate zones. Specifically, L. deliense demonstrates significant sensitivity to precipitation during both the warmest and wettest quarters, indicating a ecological preference for hot and humid tropical environments, while L. scutellare is more sensitive to precipitation of warmest quarter. Under the SSP5-8.5 scenario, the suitable habitat areas for the two species are expected to increase by 84.58% and 148.50%, respectively, compared to historical levels. Climate change will have a significant impact on the expansion of suitable habitats for these species. Conclusions To effectively prevent disease outbreaks and their spread, it is recommended to further strengthen disease surveillance and control measures in highly suitable areas where vector organisms are already present. Simultaneously, entry-exit quarantine and vector invasion monitoring capabilities should be enhanced in other highly suitable regions to prevent the introduction and establishment of foreign vector species. Scrub typhus Mite-borne disease Maximum Entropy Model Global distribution Climate change Leptotrombidium Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Scrub typhus (tsutsugamushi disease) is a natural focal zoonotic disease caused by Orientia tsutsugamushi (Ot) [ 1 ]. The chigger mites is the sole transmission vector of this disease [ 2 ]. Among these vectors, Leptotrombidium deliense ( L. deliense ) is the dominant vector species in summer-endemic areas, while Leptotrombidium scutellare ( L. scutellare ) serves as the main vector species in most autumn-winter endemic areas [ 3 ]. Studies have shown that scrub typhus is primarily distributed in the traditional 'Tsutsugamushi Triangle,' which stretches from the Russian Far East in the north, west to Pakistan, south to Australia, and east to Japan, covering numerous countries and regions in East Asia, Southeast Asia, South Asia, Pacific islands, and northeastern Australia, with an area exceeding 8 million square kilometers [ 4 ]. This region has a high population density, with approximately 1 million cases of scrub typhus reported annually worldwide, and about 1 billion people at risk of infection [ 5 ]. In addition, scrub typhus has also been reported outside the traditional triangle, particularly in the Middle East, Africa, and South America [ 6 ]. In recent years, with the acceleration of urbanization, climate change, and increased population mobility, the number of newly reported, re-emerging, recurring, and imported cases of scrub typhus worldwide has been on the rise [ 7 ]. The endemic areas of scrub typhus are expanding, and it is shifting from a regional disease to a global public health issue [ 8 ]. Studies have found that climatic factors are closely associated with the occurrence of vector-borne infectious diseases. At the same time, climate factors can also affect the population size and distribution of vector chigger mites, thereby generating new epidemic risks [ 9 ]. Natural environmental factors in endemic areas—such as temperature, precipitation, relative humidity, and average daily sunshine duration—all influence the population of chigger mites [ 10 ], among which annual average temperature and humidity have the most significant effects on the growth and reproduction of mites. A study by Tian Ma et al. [ 11 ] showed that climate, land cover, and elevation are significantly associated with the spatial distribution of L. deliense and L. scutellare . Previous studies have indicated habitat differences between the two chigger mite species. A study conducted in Yunnan, China, found that L. deliense is mainly distributed in low-altitude plains and valleys, whereas L. scutellare predominantly appears in certain high-altitude mountainous areas [ 12 ]. Additionally, L. deliense prefers hot and humid environments [ 13 ], while L. scutellare favors relatively cold and dry seasons, with high temperatures and excessive rainfall being detrimental to its survival, development, and reproduction [ 14 ]. L. scutellare shows strong cold resistance, surviving 2 months at 1–2°C and 1 month at − 20°C [ 15 ]. Due to the habitat differences between L. deliense and L. scutellare , their spatiotemporal distribution patterns also differ. L. deliense primarily inhabits most regions south of the Yangtze River in China [ 3 ], typically appearing in April, peaking between June and August, and gradually declining from September to December. It is the main vector mite species in summer-type endemic areas [ 16 ]. In contrast, L. scutellare is widely distributed across multiple regions of China, mainly in provinces north of the Yangtze River, such as Jiangsu, Shandong, and Anhui. It is the primary vector mite species in most autumn-winter-type endemic areas north of the Yangtze River. Its seasonal fluctuation pattern is opposite to that of L. deliense , typically appearing in September and peaking from October to December [ 17 ]. Furthermore, existing studies indicated that the geographical ranges and seasonal patterns of these species are shifting due to environmental changes [ 18 ]. Therefore, it is essential to understand how environmental factors—such as climate, land cover, and elevation—influence the distribution of these two chigger mite species. Ecological Niche Models (ENM) are used to assess suitable environmental conditions for species distribution and play a significant role in preventing outbreaks of mite-borne diseases. Among them, the Maximum Entropy Model (MaxEnt) stands out due to its superior predictive performance, including accurate predictions with small sample sizes and its ability to interpret the influence of spatial variables [ 19 – 21 ]. With economic globalization, urbanization, and rising personal incomes [ 22 ], coupled with climate change, pathogens have gained more opportunities for global dissemination. As a result, the risk of transmission of mite-borne diseases has increased. Currently, there is limited research on predicting the global suitable habitats for the two main scrub typhus mite species, L. deliense and L. scutellare , with most studies being confined to specific countries or regions [ 11 ]. The innovation of this study lies in the use of the MaxEnt model to analyze potential changes in the suitable habitats of these scrub typhus vector species globally under current and future climate scenarios. Although these mite species have not yet been detected in some regions, they possess potential invasion risks under favorable conditions. Therefore, this study is of great importance for preventing the invasion of these vector organisms and is crucial for controlling mite-borne diseases [ 21 ]. Methods Species Distribution We obtained species distribution records primarily from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/ , accessed on 6 April 2024). To supplement these data, we also conducted a literature search across multiple databases, including Web of Science, PubMed database, China National Knowledge Infrastructure, VIP Database and Wanfang Data Knowledge Service Platform [ 23 – 37 ]. All collected records were processed to eliminate duplicates and incomplete entries. Additionally, ArcGIS software (version 10.6, ESRI Inc., USA) was applied to exclude occurrence points with obvious geographic inaccuracies. Following these procedures, a total of 80 valid records were retained for L. deliense and 43 for L. scutellare . Environmental Variables Environmental predictors were selected based on their ecological relevance and inter-variable correlations. In this study, nineteen bioclimatic variables (1970–2000, 5 arcmin spatial resolution) were obtained from WorldClim version 2.1 Global Climate Database ( http://worldclim.org/version2 , accessed on: 13 July 2025). Meanwhile, future climate scenarios were based on simulations from the medium-resolution Beijing Climate Center Climate System Model (BCC-CSM2-MR), considering two greenhouse gas emission pathways: Shared Socio-economic Pathways (SSPs) ssp1-2.6 and ssp5-8.5. These data, covering the period 2021–2060, were also sourced from WorldClim at a 5 arcmin resolution. Topographic predictors included elevation, aspect, and slope. Elevation information was derived from the Digital Elevation Model (DEM) in WorldClim, while slope and aspect layers were generated using the Spatial Analyst Tools in ArcGIS.. To reduce multicollinearity, environmental variables were screened in two steps. First, the MaxEnt model was used to evaluate the relative contribution of each variable to species distribution, and those with higher contribution rates were retained. Second, environmental variables were resampled in ArcGIS, and Pearson correlation analysis was performed in R. For variables with a correlation coefficient greater than 0.8, only the one with the higher contribution was kept for subsequent modeling. Selection and optimization of Model Parameters All occurrence records of L. deliense and L. scutellare together with environmental variables were imported into MaxEnt3.4.3. The model was run with a random partition of 75% of records for training and 25% for testing. The output format was set to “Cloglog,” with the maximum iteration method configured as “Bootstrap” and the maximum number of replicates set to 5000. To optimize model performance and prevent overfitting, we further employed the R package “ENMeval” (R 4.1.0, using the “Checkerboard” method) to select the most suitable parameters. Candidate settings included five feature combinations (linear [L], quadratic [Q], hinge [H], product [P], and threshold [T]) and eight levels of regularization multipliers (0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4). Distribution and change of suitable area Using the Maximum Entropy Model, we combined the retained distribution records, environmental variables, and optimized parameters to project historical and future suitable areas of L. deliense and L. scutellare . Model outputs in cloglog format were classified into four suitability levels with the Jenks natural breaks method in ArcGIS: unsuitability (0-0.068), low suitability (0.068–0.240), moderate suitability (0.240–0.480) and high suitability (0.480-1). The area corresponding to each suitability category was then calculated in ArcGIS. Model performance was assessed with the ROC curve, and prediction accuracy was indicated by the AUC value, which reflects the relationship between environmental predictors and species distribution. According to the ROC evaluation criteria, an AUC of 0–0.6 indicates failure, 0.6–0.7 poor performance, 0.7–0.8 moderate reliability, and 0.8–1 good predictive ability. Results Global distribution of vectors of RMSF The results of this study revealed markedly distinct geographical distribution patterns between L. scutellare and L. deliense . L. deliense was predominantly distributed in tropical regions (between 23.5°N and 23.5°S), with concentrated populations in areas characterized by high temperature and humidity south of the Yangtze River in China, Southeast Asia, and South Asia. Its distribution showed strong congruence with Köppen climate classifications Af (tropical rainforest climate) and Am (tropical monsoon climate) (Fig. 1 ). In contrast, L. scutellare primarily occupied temperate zones (23.5°N to 66.5°N), forming core distribution areas in northern China (North China Plain), Honshu Island of Japan, and southern South Korea, with its range corresponding to Cwa (temperate climate with dry winters and hot summers) and Cfa (humid subtropical climate) zones (Fig. 1 ). The parameters of the Maximum Entropy Model Following jackknife analysis, only bioclimatic and environmental variables with a contribution rate above 1% in the MaxEnt model were retained (Fig. 2 ). The correlations among these variables were further examined using Pearson analysis. For each species, the ten variables with the greatest explanatory power were ultimately selected (Table 1 ). Optimal model parameters were determined with the R package “ENMeval” based on the lowest deltaAICc value. The refined MaxEnt models produced mean AUC values of 0.943 and 0.945, respectively (Figure S1 ). Table 1 Environmental variables used for predicting the species distribution Variables Description (Unit) Contribution ( L. deliense , %) Contribution ( L. scutellare , %) bio18 Precipitation of Warmest Quarter (mm) 39.7 59.0 bio16 Precipitation of Wettest Quarter (mm) 22.7 - elev Elevation (m) 13.7 12.7 bio10 Mean Temperature of Warmest Quarter (°C) 10.9 4 bio2 Mean Diurnal Range (°C) 3.5 2.3 bio3 Isothermality 2.8 - bio14 Precipitation of Driest Month (mm) 2.2 - bio4 Temperature Seasonality 2 13.7 slope Slope (°) 1.3 1.1 bio11 Mean Temperature of Coldest Quarter (°C) 1.1 - bio8 Mean Temperature of Wettest Quarter (°C) - 1.1 bio15 Precipitation Seasonality - 4 bio6 Min Temperature of Coldest Month (°C) - 2.3 aspect Aspect (°) - 1.6 The relationship between environment variables and the distribution of vectors of Scrub typhus For L. deliense , model results indicated that precipitation exerted a strong influence on its distribution. Precipitation of the warmest quarter (BIO18) and wettest quarter (BIO16) collectively contributed more than 60% to the model, with BIO18 alone accounting for 39.7%. The response curve showed that survival probability increased with BIO18, reaching a maximum at 1000 mm, and then declined while it remained relatively high (> 0.7). For BIO16, survival followed a unimodal pattern, peaking at 900 mm and gradually declining thereafter, stabilizing near 4000 mm. Elevation was a critical limiting factor, as survival probability decreased sharply above 2000 m and approached 0.1. The response to mean temperature of the warmest quarter (BIO10) also exhibited a unimodal pattern, with optimal survival at 25–30°C, and no survival observed below 10°C or above 35°C. The most favorable conditions for L. deliense combined BIO10 of 25–30°C with precipitation > 1000 mm, consistent with tropical monsoon climates. Meanwhile, high diurnal temperature range (BIO2) reduced the survival of L. deliense (Fig. 3 ). For L. scutellare , precipitation of the warmest quarter (BIO18) was the most important predictor, contributing more than 50% to the model. The optimal BIO18 was 500 mm, with habitat suitability decreased at both higher and lower values; survival probability approached zero when precipitation exceeded 2000 mm. Model results showed that temperature seasonality (BIO4) promoted the survival probability of L. scutellare when below 900, but reduced survival when exceeding this threshold, indicating tolerance to relatively high winter–summer differences but sensitivity to excessive variability. Elevation imposed additional constraints, with survival probability approaching zero above 3000 m. Compared with L. deliense , L. scutellare demonstrated broader tolerance to warmest-quarter temperatures (BIO10), though its maximum survival probability was lower. Precipitation seasonality (BIO15) was also influential, with optimal survival occurring within the range of 40–120; values outside this range suppressed survival (Fig. 4 ). The potential distribution of vectors of Scrub typhus under different climate scenarios Under the historical climate scenarios, the total potentially suitable distribution area for L. deliense is estimated at 31.97×10 6 km 2 , of which approximately 3.67×10 6 km 2 is classified as highly suitable area. Suitable areas for L. deliense are mainly located in south of the Yangtze River in China, Southeast Asia, South Asia, and a few parts of North Australia. The high suitable area is primarily located in the Southeast China Coast, with a few parts of Southeast Asia (Fig. 5 A). In addition, the total potentially suitable distribution area for L. scutellare is estimated at 11.01×10 6 km 2 , of which approximately 1.84×10 6 km 2 is classified as highly suitable area. Suitable areas for L. scutellare are primarily concentrated in temperate regions of southern and eastern coastal areas of China, Honshu Island of Japan, and southern South Korea (Fig. 5 B). Under the different future climate scenarios (ssp1-2.6 and ssp5-5.8), the suitable areas for both L. delicense and L. scutellare showed expansion trends (Table 2 ). For the same time period, the suitable area under the SSP5-8.5 scenario was consistently larger than that under the SSP1-2.6 scenario. Furthermore, the suitable areas gradually increased over time. The maximum total suitable habitat areas for L. deliense and L. scutellare were projected to be 59.01×10 6 km 2 and 27.36×10 6 km 2 respectively, representing increases of 84.58% and 148.50% compared to their historical climate scenario distributions. Table 2 Suitable areas for vectors of Scrub typhus across the world under different climatic scenarios (×10 6 km 2 ) Climate Scenarios Period Less suitability Moderately suitability High suitability Total area Area change Area change Rate (%) L. deliense Historical 1970–2000 20.17 8.13 3.67 31.97 - - ssp1-2.6 2021–2040 27.98 12.42 6.61 47.01 15.04 47.04 2041–2060 27.03 13.72 7.59 48.34 16.37 51.20 ssp5-8.5 2021–2040 27.61 13.33 6.91 47.58 15.61 48.83 2041–2060 32.21 16.24 10.56 59.01 27.04 84.58 L. scutellare Historical 1970–2000 7.44 1.73 1.84 11.01 - - ssp1-2.6 2021–2040 15.65 2.59 1.93 20.17 9.16 83.20 2041–2060 16.99 2.68 2.08 21.75 10.74 97.55 ssp5-8.5 2021–2040 16.05 2.71 2.05 20.81 9.8 89.01 2041–2060 21.54 3.41 2.41 27.36 16.35 148.50 Discussion This study systematically elucidates the global distribution patterns and climatic drivers of the two major scrub typhus vectors - Leptotrombidium deliense and L. scutellare . Regarding spatial distribution characteristics, while both mite species are predominantly distributed in Asia, ecological niche modeling predicts potential suitable habitats across all continents except Antarctica. This distribution pattern demonstrates significant interspecific niche differentiation: L. deliense primarily adapts to tropical climate zones, whereas L. scutellare shows preference for temperate environments. This divergence reflects their distinct climate adaptation strategies developed through long-term evolution. In terms of climatic driving mechanisms, precipitation was identified as the key factor influencing their distributions. For L. deliense , the combined contribution of precipitation in the warmest and wettest quarters exceeds 60% [38], indicating strict dependence on summer rainfall. The species achieves peak survival probability in tropical monsoon climate zones (e.g., Southeast Asian rice paddies) with annual precipitation exceeding 1000 mm and even seasonal distribution [10]. This ecological requirement is further supported by the newborn larvae's specific preference for warm (28-30°C) and humid (85-90% RH) microhabitats [39]. In contrast, the distribution of L. scutellare is mainly regulated by altitude [40], typically found in high-altitude mountainous areas characterized by low temperature and precipitation, demonstrating greater environmental tolerance. Regarding ecological adaptation mechanisms, the study reveals both commonalities and interspecific differences. Both species reach maximum population density under moderate precipitation levels, which correlates closely with their biological traits. On one hand, Yang's (1992) research confirmed the aquatic adaptability of L. scutellare larvae [41]; on the other hand, Rose et al.(2001) demonstrated that extreme precipitation can destroy mite egg chambers or disrupt ground microhabitats [42], collectively forming the "intermediate precipitation optimum" phenomenon. Simultaneously, interspecific differences are evident: L. deliense strictly depends on regular monsoon precipitation (BIO16=900-4000mm), while L. scutellare can tolerate broader precipitation variability (BIO15=40-120), reflecting typical adaptation strategies of tropical versus temperate species. These precipitation-mediated distribution patterns are further modulated by the synergistic effects of temperature seasonality and altitudinal gradients, ultimately forming well-defined biogeographic distribution patterns. This study not only deepens our understanding of the ecological adaptation mechanisms of vector mites but, more importantly, provides a scientific basis for predicting scrub typhus transmission risks under climate change scenarios, offering significant guidance for developing targeted prevention and control strategies. Early identification and elimination of Leptotrombidium mite breeding sites (such as stagnant water bodies) should be carried out through measures like larviciding, drainage, or habitat modification. For high-risk groups such as farmers working in the field, personal protective equipment (PPE) should be promoted [43], and public health education should be conducted to raise awareness. Improving the knowledge, attitudes, and practices of healthcare workers and residents regarding scrub typhus prevention and control can help prevent outbreaks and epidemics to some extent, thus alleviating the burden of scrub typhus[44]. The suitable habitats of the two vectors are projected to expand in future periods, which may be associated with global warming. As the climate continues to warm, mite habitats are likely to gradually extend toward colder polar regions, thereby further enlarging the endemic areas of scrub typhus and posing ongoing threats to public health [45]. Governments should pay close attention to greenhouse gas emissions in order to prevent the further expansion of zoonotic disease-suitable areas. This study has several limitations. Vector survival is also affected by ecological factors such as vegetation cover, soil moisture, and host density, which were not included in the model. The occurrence data may be incomplete because of the limited scope of the literature search, which could influence the accuracy of the predictions. In addition, L. deliense and L. scutellare are not the only vectors of scrub typhus, and future research should examine the global distribution of other important mite species. Conclusion This study found that the two vectors of scrub typhus are suitable for survival on all continents except Antarctica. L. deliense is more adapted to tropical regions, whereas L. scutellare is better suited to temperate regions. Under future climate conditions, the suitable ranges of both vectors are expected to expand significantly. With ongoing climate change and global economic integration, the likelihood of scrub typhus vectors being introduced into more countries will increase. This indicates the need for early prediction and warning of vector invasion risks. Declarations Ethical approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Vector Surveillance and Control Project (No. 102393220020020000012). Authors’ contributions Author Contributions Conceptualization: Xinning Hao, Qiyong Liu. Data curation: Xinning Hao, Lianfang Feng, Qiyong Liu. Formal analysis: Xinning Hao. Funding acquisition: Qiyong Liu. Methodology: Xinning Hao. Software: Xinning Hao, Lianfang Feng, Zihang Wang. Supervision: Qiyong Liu, Haoqiang Ji. Validation: Qiyong Liu, Lianfang Feng, Qiyong Liu. Visualization: Xinning Hao. Writing –original draft: Xinning Hao. Writing –review & editing: Xinning Hao, Lianfang Feng, Zihang Wang, Haoqiang Ji, Shengping Dou, Ning Zhao, Qiyong Liu. All authors read and approved the final manuscript. Acknowledgments We thank the groups of ArcGIS, MaxEnt, GBIF and WorldClim for their contributions to the completion of this study. Authors' information Xinning Hao is a postgraduate student in the Department of Vector Biology and Control, School of Public Health, Shandong University. She is currently undertaking an internship at the National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, and has participated in several research projects on vectors and climate change. References Mahajan SK. Scrub typhus. J Assoc Physicians India. 2005;53(11):954–958. Olsen SJ, Campbell AP, Supawat K, et al. Infectious causes of encephalitis and meningoencephalitis in Thailand, 2003–2005. Emerg Infect Dis. 2015;21(2):280–287. Li W, Li GC, Liu XB, Liu QY, Lu L. Research progress in epidemiological characteristics and influencing factors of scrub typhus. Chin J Vector Biol Control. 2020;31(6):738–743. Xu G, Walker DA-O, Jupiter D, Melby PC, Arcari CM. A review of the global epidemiology of scrub typhus. PLoS Negl Trop Dis. 2017;11(11):e0006062. doi: 10.1371/journal.pntd.0006062 . Ren J, Sun J, Wang Z, Ling F, Shi X, Zhang R, et al. Re-emergence of scrub typhus in Zhejiang Province, southern China: a 45-year population-based surveillance study. Travel Med Infect Dis. 2019;32:101495. doi: 10.1016/j.tmaid.2019.101495 . Elders PND, Penna da Silva L, Parquet T, et al. Diagnostic accuracy of an in-house Scrub Typhus enzyme linked immunoassay for the detection of IgM and IgG antibodies in Laos. PLoS Negl Trop Dis. 2020;14(12):e0008858. Wang W, Yang JJ, Zhong YP, et al. Research progress on special clinical features and clinical diagnosis and treatment of ascariasis. Chin J Infect Dis. 2021;39(12):707–710. doi: 10.3760/cmaj.cn311365-20210222-00065 . Li GC, Liu QY. Epidemiological status of ascariasis. Dis Surveill. 2018;33(2):129–138. doi: 10.3784/i.issn.1003-9961.2018.02.006 . Liu Q, Xu W, Lu S, et al. Landscape of emerging and re-emerging infectious diseases in China: impact of ecology, climate, and behavior. Front Med. 2018;12:3–22. doi: 10.1007/s11684-018-0613-5 Sponchiado J, Melo G, Martins T, Krawczak F, Jacinavicius FC, Labruna M, et al. Ectoparasites of small mammals: determinants of community structure in South American savannah. Parasitology. 2017;144(4):475–483. Ma T, Hao M, Chen S, Ding F. The current and future risk of spread of Leptotrombidium deliense and Leptotrombidium scutellare in mainland China. Sci Total Environ. 2022;843:156986. doi: 10.1016/j.scitotenv.2022.156986 Peng PY, Han TW, Wang JX, et al. Species diversity of ectoparasitic chigger mites (Acari: Prostigmata) on small mammals in Yunnan Province, China. Parasitol Res. 2016;115(9):3605–3618. doi: 10.1007/s00436-016-5097-7 Phasomkusolsil S, Tanskul P, Ratanatham S, Watcharapichat P, Phulsuksombati D, Frances SP, et al. Influence of Orientia tsutsugamushi infection on the developmental biology of Leptotrombidium imphalum and L. chiangraiensis (Acari: Trombiculidae). J Med Entomol. 2014;49(6):1270–1275. Lv Y, et al. Infestation and seasonal fluctuation of chigger mites on the Southeast Asian house rat (Rattus brunneusculus) in southern Yunnan Province, China. Int J Parasitol Parasites Wildl. 2021;14:141–149. doi: 10.1016/j.ijppaw.2021.01.006 Suzuki T. Studies on the bionomics and chemical control of tsutsugamushi (scrub typhus mites). II. Trombicula scutellaris Nagayo et al. in southern Kanto of Japan. Jpn J Exp Med. 1954;24(4):181–197. Geng, M.L., et al., 2014. Advances in the study of Leptorombidium deliense in China. J. Pathog. Biol. 9 (08), 763–765 Liu YX, et al. Clinical characteristics of the autumn–winter type scrub typhus cases in south of Shandong province, northern China. BMC Infect Dis. 2009;9:82. doi: 10.1186/1471-2334-9-82 Cao M, et al. Spring scrub typhus, People’s Republic of China. Emerg Infect Dis. 2006;12(9):1361–1363. doi: 10.3201/eid1209.051465 Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Modell. 2006;190(3–4):231–259. doi: 10.1016/j.ecolmodel.2005.03.026 Barber RA, Gillingham PK, Hannah L, et al. Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Divers Distrib. 2022;28(1):128–141. doi: 10.1111/ddi.13356 Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr. 2007;34(1):102–117. doi: 10.1111/j.1365-2699.2006.01594.x Wu T, Perrings C, Kinzig A, Collins JP, Minteer BA, Daszak P. Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: a review. Ambio. 2017;46(1):18–29. doi: 10.1007/s13280-016-0809-2 Li JC. Research on vectors and pathogens of chigger mites in China. Guangzhou: Guangdong Science & Technology Press; 1997:1–570. Zhan HY, Xu Z, He YL. Epidemiological characteristics and influencing factors of scrub typhus in Jingjiang City of Jiangsu Province, 2011–2015. Chin J Dis Control Prev. 2017;21(11):1119–1122. doi: 10.16462/j.cnki.zhjbkz.2017.11.010 Zhou SH, Han TW, Wang JX, et al. Investigation of chigger mite species diversity parasitizing wild rodents in some areas of Fujian Province. Chin J Zoonoses. 2021;37(6):511–519. doi: 10.3969/j.issn.1002-2694.2021.00.076 Hou SX, Guo XG, Men XY, et al. A preliminary checklist of chigger mites in Yunnan Province, China. Acta Arachnol Sin. 2006;15(2):114–122. doi: 10.3969/j.issn.1005-9628.2006.02.012 Huang Y, Zhao L, Zhang Z, et al. Detection of a novel Rickettsia from Leptotrombidium scutellare mites (Acari: Trombiculidae) from Shandong, China. J Med Entomol. 2017;54(3):544–549. doi: 10.1093/jme/tjw234 Zhao L, Zhu H, Zhang LS, et al. Ectoparasitic mites and Orientia tsutsugamushi infection in wild rodents in Yingshang County, Anhui Province. Chin J Pathog Biol. 2022;17(9):1061–1063,1076. doi: 10.13350/j.cjpb.22091 Lu XC, Cai XY, Lin C, et al. Population and chigger mite carriage status of wild rodents in Pudong New Area, Shanghai. Shanghai J Prev Med. 2023;35(12):1218–1222. doi: 10.19428/j.cnki.sjpm.2023.23209 Chung LH, Wu WJ, Kuo CC, Wang HC. A checklist of chigger mites (Acari: Trombiculidae and Leeuwenhoekiidae) from Taiwan, with descriptions of three new species. J Med Entomol. 2015;52(6):1241–1253. Ding F, Jiang WL, Guo XG, et al. Infestation and related ecology of chigger mites on the Asian house rat (Rattus tanezumi) in Yunnan Province, Southwest China. Korean J Parasitol. 2021;59(4):377–392. doi: 10.3347/kjp.2021.59.4.377 Guo Y, Zhou JX, Guo XG, et al. Species diversity and related ecology of chiggers on small mammals in a unique geographical area of Yunnan Province, Southwest China. Exp Appl Acarol. 2023;91:439–461. doi: 10.1007/s10493-023-00841-z Yu ES. Analysis of current epidemiological characteristics of scrub typhus in China. Chin J Epidemiol. 1997;18(1):56. Wei L, Wang XW, Wang CM, et al. Ecological survey and analysis of small mammals and ectoparasites in six regions of Sichuan Province. Chin J Vector Biol Control. 2014;25(4):309–313. Zhan YZ, Guo XG, Zuo XH, et al. Distribution of Leptotrombidium spp. in parts of Yunnan Province, China. Chin J Epidemiol. 2011;32(1):13–16. doi: 10.3760/cma.j.issn.0254-6450.2011.01.004 Li GC, Wang YJ, Yue YJ, et al. Delineation of summer- and autumn-type scrub typhus endemic areas in China. Chin J Vector Biol Control. 2019;30(3):233–236. Liao H, Dai CW, Wang J. Host distribution and infection status of scrub typhus in Nanshan District, Shenzhen City. Jiangsu Prev Med. 2016;27(4):496–497. Lv Y, Guo XG, Jin DC. Research Progress on Leptotrombidium deliense. Korean J Parasitol. 2018;56(4):313–324. doi: 10.3347/kjp.2018.56.4.313 Varma RN, Mahadevan B. Laboratory colonisation of the scrub ty-phus vector, Leptotrombidium deliense (L.) and some observations on its life history and habits. Indian J Entomol 1974; 34: 300–306. Zhan YZ, Guo XG, Zuo XH, Wang QH, Wu Q. Research on the area distribution and host selection of Leptotrombidium scutellarein 19 counties of Yunnan Province. Chin J Parasitol Parasitic Dis 2011; 29: 393–396 (in Chinese). Yang GR. Observation on the vitality of Leptorombidium scutellarelarvae in water. Chin J Vector Bio Control 1992; 6: 395 (in Chi-nese). Rose, et al., 2001. Climate variability and change in the United States: potential impacts on water- and foodborne diseases caused by microbiologic agents. Environ. Health Perspect. 109 (2), 211–221. Zhou Y, Yang XQ, Ming XL, et al. The epidemic situation and prevention and control status of scrub typhus. Chin J Zoonoses. 2023;39(10):1024–1031. Xie XF, Wang GY, Huang Y, et al. Advances in clinical research and epidemiology of scrub typhus in China (2010–2020). Hainan Med J. 2023;29(19):1505–1509. Ma DL, Lun XC, Li C, Zhou RB, Zhao Z, Wang J, et al. Predicting the Potential Global Distribution of Amblyomma americanum (Acari: Ixodidae) under Near Current and Future Climatic Conditions, Using the Maximum Entropy Model. Biology-Basel. 2021;10(10):16. doi: 10.3390/biology10101057 . PubMed PMID: WOS:000716309100001 Additional Declarations No competing interests reported. Supplementary Files Appendix9.3.docx Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Parasitology Research → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 11 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7592864","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516147676,"identity":"134ebd71-8a4d-43a4-a12f-8c1449f4ed2e","order_by":0,"name":"Xinning Hao","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xinning","middleName":"","lastName":"Hao","suffix":""},{"id":516147677,"identity":"1022c90b-87ea-4389-964d-d97b22b13e7f","order_by":1,"name":"Lianfang Feng","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lianfang","middleName":"","lastName":"Feng","suffix":""},{"id":516147681,"identity":"f94f9782-5b70-4d57-afb5-53d71391fca3","order_by":2,"name":"Zihang Wang","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zihang","middleName":"","lastName":"Wang","suffix":""},{"id":516147684,"identity":"452a5059-54a9-4e39-a7a7-099c592cef27","order_by":3,"name":"Haoqiang Ji","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Haoqiang","middleName":"","lastName":"Ji","suffix":""},{"id":516147688,"identity":"263c969d-7923-4365-bf75-25bab4b0c646","order_by":4,"name":"Shengping Dou","email":"","orcid":"","institution":"National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Shengping","middleName":"","lastName":"Dou","suffix":""},{"id":516147691,"identity":"a55fe3c6-8849-4204-bf4d-6b4cd6b6b6ce","order_by":5,"name":"Ning Zhao","email":"","orcid":"","institution":"National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Zhao","suffix":""},{"id":516147692,"identity":"484b3b82-830c-45cc-aa7a-862733c15cf7","order_by":6,"name":"Qiyong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPmYgkQBisTc2MPAQo4UNroXnILFa4CwJoEbitLDzGN542GaTJx/5uPHDmxoGeXPCDuMxtkhsSys2vJ3YLDnnGIPhzgbCWswkEtsOJ26cndjGzNvAkGBwgGgtMw+SqmW+BCPRWtiKLRLOpSVu4AH7RcJwAyEt/PyHN978UWaTOL/9+ENgiNnIE7QFBCQYgbEDVSlBhHqwsj8MDPINxCkeBaNgFIyCEQgAFIQ61MeBOWsAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Qiyong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-09-11 14:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7592864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7592864/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00436-025-08602-0","type":"published","date":"2025-12-09T15:59:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91871774,"identity":"d26bb756-84ba-43ee-b1ec-6c7e0286b39d","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":647587,"visible":true,"origin":"","legend":"","description":"","filename":"PredictingtheGlobalPotentialDistributionofTowMajorVectorsofScrubTyphusUnderGlobalClimateChange9.11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/7c3f1ea12207eadd456034ff.docx"},{"id":91871769,"identity":"bc97857c-b29f-4ccd-85b1-9c5aa6651cda","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9435,"visible":true,"origin":"","legend":"","description":"","filename":"81c36441c13f40e69fc667a84ee42f66.json","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/92d143b69eaae7db6c8bbc48.json"},{"id":91872413,"identity":"78332bb8-f36e-4426-b8ec-8b0d732b3b60","added_by":"auto","created_at":"2025-09-22 14:07:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":952500,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix9.3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/cd9a26a53fd24cc6336b1a47.docx"},{"id":91871770,"identity":"d7cdc5e8-1576-47cc-8bb6-b9b830235518","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115552,"visible":true,"origin":"","legend":"","description":"","filename":"81c36441c13f40e69fc667a84ee42f661enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/c9343349ee0d9cd8fd06b53b.xml"},{"id":91874061,"identity":"f9c28ede-b834-4fc5-b63a-9182cad3e90f","added_by":"auto","created_at":"2025-09-22 14:15:42","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98657,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/2ec22277e930f528b8536e6d.png"},{"id":91872409,"identity":"d09f0fc3-9457-4c33-b320-2a4b65b0c21a","added_by":"auto","created_at":"2025-09-22 14:07:42","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":240817,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/e4e8ef1a67b51a21644f7408.png"},{"id":91875158,"identity":"6f9eb62d-fd06-4ed2-abed-f490902d6ad7","added_by":"auto","created_at":"2025-09-22 14:24:12","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82870,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/246bf66a15e081d9501b7623.png"},{"id":91872417,"identity":"02308495-18d9-481c-8b96-a99dcff401f5","added_by":"auto","created_at":"2025-09-22 14:07:43","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":782333,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/a9d5d4066d539b56b976cfe3.jpeg"},{"id":91875108,"identity":"46a0e90c-15e1-4539-a77e-e5112e868e00","added_by":"auto","created_at":"2025-09-22 14:24:08","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5755,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/54695a2cf840e6ef8543d037.jpeg"},{"id":91871791,"identity":"585a5ac1-fbd3-43af-a6e1-7a83d3599333","added_by":"auto","created_at":"2025-09-22 13:59:43","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":417332,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/1a9c2b1b81930170e562f766.jpeg"},{"id":91871789,"identity":"c444883f-2153-45f8-8c10-7c0bbc61c58b","added_by":"auto","created_at":"2025-09-22 13:59:43","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/253578152b9d12d3c1d625e5.jpeg"},{"id":91875145,"identity":"8a4cb8e8-a90c-4a54-8fe9-8d6fc7751862","added_by":"auto","created_at":"2025-09-22 14:24:11","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25630,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/bc8a4e3c7a66415a5699cdd9.png"},{"id":91874064,"identity":"7ec3cba2-ed4c-4301-bd79-0895c71d9b01","added_by":"auto","created_at":"2025-09-22 14:15:43","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38818,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/3054b97256fe9b83f861a572.png"},{"id":91872419,"identity":"f505e9ab-2402-4137-881c-00a2f8bce8fc","added_by":"auto","created_at":"2025-09-22 14:07:43","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18034,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/a3c970e20d74c0d5fb498d23.png"},{"id":91871783,"identity":"2e8d26cb-3fae-41f5-8e22-bd1b3f2a86f9","added_by":"auto","created_at":"2025-09-22 13:59:43","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146991,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/b776cac7b5e021ef509ab117.png"},{"id":91871781,"identity":"eccac0ac-574d-4c65-bf4b-992f642f2a5a","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1943,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/4f38211e6269df46d4a687b0.png"},{"id":91872416,"identity":"4a87676b-7b31-4065-9793-01e9a452737e","added_by":"auto","created_at":"2025-09-22 14:07:43","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84651,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/737ae71e7a90e7ebb0e8ae95.png"},{"id":91871790,"identity":"cf71d988-ca7e-45a4-9abb-88b1519ce2a3","added_by":"auto","created_at":"2025-09-22 13:59:43","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/407ace593a0b2c8d8975ebda.png"},{"id":91871793,"identity":"eff66bf7-b3fc-47f7-8b7b-196ae70ec061","added_by":"auto","created_at":"2025-09-22 13:59:43","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114816,"visible":true,"origin":"","legend":"","description":"","filename":"81c36441c13f40e69fc667a84ee42f661structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/a43961861c92b0279a7fbfbc.xml"},{"id":91871787,"identity":"13970cce-7920-422b-b761-b477531dc34a","added_by":"auto","created_at":"2025-09-22 13:59:43","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125983,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/9ea2f50b6e9d5bba3ba27cee.html"},{"id":91872407,"identity":"8e360693-3260-4c3e-95f7-93e7c15a456e","added_by":"auto","created_at":"2025-09-22 14:07:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87435,"visible":true,"origin":"","legend":"\u003cp\u003eCurrrent global distribution of \u003cem\u003eL. delicense\u003c/em\u003e and\u003cem\u003e L. scutellare\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/eeae02ea9aa30a03ea03194b.png"},{"id":91871767,"identity":"47933231-ecdd-46f2-a3d5-da39e1a2ebbe","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244138,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of Pearson’s correlation coefficient (A: \u003cem\u003eL. deliense\u003c/em\u003e; B: \u003cem\u003eL. scutellare\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/fb9a36edc307b5a893f5f03b.png"},{"id":91871768,"identity":"19db7ee5-d0e5-4a34-bb65-08c687314f99","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84505,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of environmental variables to the distribution probability of \u003cem\u003eL. deliense\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/6402e210c1718eb74c899222.png"},{"id":91874059,"identity":"91f609c5-efcc-452f-9835-a3eaf3701311","added_by":"auto","created_at":"2025-09-22 14:15:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82581,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of environmental variables to the distribution probability of \u003cem\u003eL. scutellare\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/348394e700f75d6852e79e61.png"},{"id":91871773,"identity":"96d00209-da2c-4af3-b9d2-b28e47013f9f","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":179655,"visible":true,"origin":"","legend":"\u003cp\u003ePotentially suitable area under historical situation. (Figure A:\u003cem\u003e L. deliense\u003c/em\u003e; Figure B:\u003cem\u003e L. scutellare\u003c/em\u003e)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/9d845176eec310c29e86dc4b.png"},{"id":98245117,"identity":"030ff0fa-d986-43fa-9901-21b906274dd8","added_by":"auto","created_at":"2025-12-15 16:16:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1431009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/95e10067-73f7-4e71-b063-28bf35fd95a4.pdf"},{"id":91871776,"identity":"de2abc83-b7da-4050-aa24-19f0e06cc545","added_by":"auto","created_at":"2025-09-22 13:59:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":952500,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix9.3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7592864/v1/80c77ebd8d7c64f6dc52df55.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the Global Potential Distribution of Tow Major Vectors of Scrub Typhus Under Future Climate Scenarios","fulltext":[{"header":"Background","content":"\u003cp\u003eScrub typhus (tsutsugamushi disease) is a natural focal zoonotic disease caused by Orientia tsutsugamushi (Ot) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The chigger mites is the sole transmission vector of this disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these vectors, \u003cem\u003eLeptotrombidium deliense\u003c/em\u003e (\u003cem\u003eL. deliense\u003c/em\u003e) is the dominant vector species in summer-endemic areas, while \u003cem\u003eLeptotrombidium scutellare\u003c/em\u003e (\u003cem\u003eL. scutellare\u003c/em\u003e) serves as the main vector species in most autumn-winter endemic areas [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have shown that scrub typhus is primarily distributed in the traditional 'Tsutsugamushi Triangle,' which stretches from the Russian Far East in the north, west to Pakistan, south to Australia, and east to Japan, covering numerous countries and regions in East Asia, Southeast Asia, South Asia, Pacific islands, and northeastern Australia, with an area exceeding 8\u0026nbsp;million square kilometers [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This region has a high population density, with approximately 1\u0026nbsp;million cases of scrub typhus reported annually worldwide, and about 1\u0026nbsp;billion people at risk of infection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, scrub typhus has also been reported outside the traditional triangle, particularly in the Middle East, Africa, and South America [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, with the acceleration of urbanization, climate change, and increased population mobility, the number of newly reported, re-emerging, recurring, and imported cases of scrub typhus worldwide has been on the rise [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The endemic areas of scrub typhus are expanding, and it is shifting from a regional disease to a global public health issue [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStudies have found that climatic factors are closely associated with the occurrence of vector-borne infectious diseases. At the same time, climate factors can also affect the population size and distribution of vector chigger mites, thereby generating new epidemic risks [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Natural environmental factors in endemic areas\u0026mdash;such as temperature, precipitation, relative humidity, and average daily sunshine duration\u0026mdash;all influence the population of chigger mites [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], among which annual average temperature and humidity have the most significant effects on the growth and reproduction of mites. A study by Tian Ma et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that climate, land cover, and elevation are significantly associated with the spatial distribution of \u003cem\u003eL. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies have indicated habitat differences between the two chigger mite species. A study conducted in Yunnan, China, found that \u003cem\u003eL. deliense\u003c/em\u003e is mainly distributed in low-altitude plains and valleys, whereas \u003cem\u003eL. scutellare\u003c/em\u003e predominantly appears in certain high-altitude mountainous areas [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, \u003cem\u003eL. deliense\u003c/em\u003e prefers hot and humid environments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], while \u003cem\u003eL. scutellare\u003c/em\u003e favors relatively cold and dry seasons, with high temperatures and excessive rainfall being detrimental to its survival, development, and reproduction [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003eL. scutellare\u003c/em\u003e shows strong cold resistance, surviving 2 months at 1\u0026ndash;2\u0026deg;C and 1 month at \u0026minus;\u0026thinsp;20\u0026deg;C [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDue to the habitat differences between \u003cem\u003eL. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e, their spatiotemporal distribution patterns also differ. \u003cem\u003eL. deliense\u003c/em\u003e primarily inhabits most regions south of the Yangtze River in China [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], typically appearing in April, peaking between June and August, and gradually declining from September to December. It is the main vector mite species in summer-type endemic areas [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, \u003cem\u003eL. scutellare\u003c/em\u003e is widely distributed across multiple regions of China, mainly in provinces north of the Yangtze River, such as Jiangsu, Shandong, and Anhui. It is the primary vector mite species in most autumn-winter-type endemic areas north of the Yangtze River. Its seasonal fluctuation pattern is opposite to that of \u003cem\u003eL. deliense\u003c/em\u003e, typically appearing in September and peaking from October to December [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, existing studies indicated that the geographical ranges and seasonal patterns of these species are shifting due to environmental changes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, it is essential to understand how environmental factors\u0026mdash;such as climate, land cover, and elevation\u0026mdash;influence the distribution of these two chigger mite species.\u003c/p\u003e\u003cp\u003eEcological Niche Models (ENM) are used to assess suitable environmental conditions for species distribution and play a significant role in preventing outbreaks of mite-borne diseases. Among them, the Maximum Entropy Model (MaxEnt) stands out due to its superior predictive performance, including accurate predictions with small sample sizes and its ability to interpret the influence of spatial variables [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith economic globalization, urbanization, and rising personal incomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], coupled with climate change, pathogens have gained more opportunities for global dissemination. As a result, the risk of transmission of mite-borne diseases has increased. Currently, there is limited research on predicting the global suitable habitats for the two main scrub typhus mite species, \u003cem\u003eL. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e, with most studies being confined to specific countries or regions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The innovation of this study lies in the use of the MaxEnt model to analyze potential changes in the suitable habitats of these scrub typhus vector species globally under current and future climate scenarios. Although these mite species have not yet been detected in some regions, they possess potential invasion risks under favorable conditions. Therefore, this study is of great importance for preventing the invasion of these vector organisms and is crucial for controlling mite-borne diseases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\n\u003ch3\u003eSpecies Distribution\u003c/h3\u003e\n\u003cp\u003eWe obtained species distribution records primarily from the Global Biodiversity Information Facility (GBIF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 6 April 2024). To supplement these data, we also conducted a literature search across multiple databases, including Web of Science, PubMed database, China National Knowledge Infrastructure, VIP Database and Wanfang Data Knowledge Service Platform [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All collected records were processed to eliminate duplicates and incomplete entries. Additionally, ArcGIS software (version 10.6, ESRI Inc., USA) was applied to exclude occurrence points with obvious geographic inaccuracies. Following these procedures, a total of 80 valid records were retained for \u003cem\u003eL. deliense\u003c/em\u003e and 43 for \u003cem\u003eL. scutellare\u003c/em\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental Variables\u003c/h2\u003e\u003cp\u003eEnvironmental predictors were selected based on their ecological relevance and inter-variable correlations. In this study, nineteen bioclimatic variables (1970\u0026ndash;2000, 5 arcmin spatial resolution) were obtained from WorldClim version 2.1 Global Climate Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://worldclim.org/version2\u003c/span\u003e\u003cspan address=\"http://worldclim.org/version2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on: 13 July 2025). Meanwhile, future climate scenarios were based on simulations from the medium-resolution Beijing Climate Center Climate System Model (BCC-CSM2-MR), considering two greenhouse gas emission pathways: Shared Socio-economic Pathways (SSPs) ssp1-2.6 and ssp5-8.5. These data, covering the period 2021\u0026ndash;2060, were also sourced from WorldClim at a 5 arcmin resolution.\u003c/p\u003e\u003cp\u003eTopographic predictors included elevation, aspect, and slope. Elevation information was derived from the Digital Elevation Model (DEM) in WorldClim, while slope and aspect layers were generated using the Spatial Analyst Tools in ArcGIS..\u003c/p\u003e\u003cp\u003eTo reduce multicollinearity, environmental variables were screened in two steps. First, the MaxEnt model was used to evaluate the relative contribution of each variable to species distribution, and those with higher contribution rates were retained. Second, environmental variables were resampled in ArcGIS, and Pearson correlation analysis was performed in R. For variables with a correlation coefficient greater than 0.8, only the one with the higher contribution was kept for subsequent modeling.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSelection and optimization of Model Parameters\u003c/h3\u003e\n\u003cp\u003eAll occurrence records of \u003cem\u003eL. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e together with environmental variables were imported into MaxEnt3.4.3. The model was run with a random partition of 75% of records for training and 25% for testing. The output format was set to \u0026ldquo;Cloglog,\u0026rdquo; with the maximum iteration method configured as \u0026ldquo;Bootstrap\u0026rdquo; and the maximum number of replicates set to 5000. To optimize model performance and prevent overfitting, we further employed the R package \u0026ldquo;ENMeval\u0026rdquo; (R 4.1.0, using the \u0026ldquo;Checkerboard\u0026rdquo; method) to select the most suitable parameters. Candidate settings included five feature combinations (linear [L], quadratic [Q], hinge [H], product [P], and threshold [T]) and eight levels of regularization multipliers (0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4).\u003c/p\u003e\n\u003ch3\u003eDistribution and change of suitable area\u003c/h3\u003e\n\u003cp\u003eUsing the Maximum Entropy Model, we combined the retained distribution records, environmental variables, and optimized parameters to project historical and future suitable areas of \u003cem\u003eL. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e. Model outputs in cloglog format were classified into four suitability levels with the Jenks natural breaks method in ArcGIS: unsuitability (0-0.068), low suitability (0.068\u0026ndash;0.240), moderate suitability (0.240\u0026ndash;0.480) and high suitability (0.480-1). The area corresponding to each suitability category was then calculated in ArcGIS. Model performance was assessed with the ROC curve, and prediction accuracy was indicated by the AUC value, which reflects the relationship between environmental predictors and species distribution. According to the ROC evaluation criteria, an AUC of 0\u0026ndash;0.6 indicates failure, 0.6\u0026ndash;0.7 poor performance, 0.7\u0026ndash;0.8 moderate reliability, and 0.8\u0026ndash;1 good predictive ability.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eGlobal distribution of vectors of RMSF\u003c/h2\u003e\n\u003cp\u003eThe results of this study revealed markedly distinct geographical distribution patterns between \u003cem\u003eL. scutellare\u003c/em\u003e and \u003cem\u003eL. deliense\u003c/em\u003e. \u003cem\u003eL. deliense\u003c/em\u003e was predominantly distributed in tropical regions (between 23.5\u0026deg;N and 23.5\u0026deg;S), with concentrated populations in areas characterized by high temperature and humidity south of the Yangtze River in China, Southeast Asia, and South Asia. Its distribution showed strong congruence with K\u0026ouml;ppen climate classifications Af (tropical rainforest climate) and Am (tropical monsoon climate) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, \u003cem\u003eL. scutellare\u003c/em\u003e primarily occupied temperate zones (23.5\u0026deg;N to 66.5\u0026deg;N), forming core distribution areas in northern China (North China Plain), Honshu Island of Japan, and southern South Korea, with its range corresponding to Cwa (temperate climate with dry winters and hot summers) and Cfa (humid subtropical climate) zones (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eThe parameters of the Maximum Entropy Model\u003c/h2\u003e\n\u003cp\u003eFollowing jackknife analysis, only bioclimatic and environmental variables with a contribution rate above 1% in the MaxEnt model were retained (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The correlations among these variables were further examined using Pearson analysis. For each species, the ten variables with the greatest explanatory power were ultimately selected (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Optimal model parameters were determined with the R package \u0026ldquo;ENMeval\u0026rdquo; based on the lowest deltaAICc value. The refined MaxEnt models produced mean AUC values of 0.943 and 0.945, respectively (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEnvironmental variables used for predicting the species distribution\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription (Unit)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eContribution\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eL. deliense\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eContribution\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eL. scutellare\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecipitation of Warmest Quarter (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecipitation of Wettest Quarter (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eelev\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElevation (m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean Temperature of Warmest Quarter (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean Diurnal Range (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIsothermality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecipitation of Driest Month (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature Seasonality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eslope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSlope (\u0026deg;)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean Temperature of Coldest Quarter (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean Temperature of Wettest Quarter (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrecipitation Seasonality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebio6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMin Temperature of Coldest Month (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003easpect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAspect (\u0026deg;)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eThe relationship between environment variables and the distribution of vectors of Scrub typhus\u003c/h3\u003e\n\u003cp\u003eFor \u003cem\u003eL. deliense\u003c/em\u003e, model results indicated that precipitation exerted a strong influence on its distribution. Precipitation of the warmest quarter (BIO18) and wettest quarter (BIO16) collectively contributed more than 60% to the model, with BIO18 alone accounting for 39.7%. The response curve showed that survival probability increased with BIO18, reaching a maximum at 1000 mm, and then declined while it remained relatively high (\u0026gt;\u0026thinsp;0.7). For BIO16, survival followed a unimodal pattern, peaking at 900 mm and gradually declining thereafter, stabilizing near 4000 mm. Elevation was a critical limiting factor, as survival probability decreased sharply above 2000 m and approached 0.1. The response to mean temperature of the warmest quarter (BIO10) also exhibited a unimodal pattern, with optimal survival at 25\u0026ndash;30\u0026deg;C, and no survival observed below 10\u0026deg;C or above 35\u0026deg;C. The most favorable conditions for \u003cem\u003eL. deliense\u003c/em\u003e combined BIO10 of 25\u0026ndash;30\u0026deg;C with precipitation\u0026thinsp;\u0026gt;\u0026thinsp;1000 mm, consistent with tropical monsoon climates. Meanwhile, high diurnal temperature range (BIO2) reduced the survival of \u003cem\u003eL. deliense\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eL. scutellare\u003c/em\u003e, precipitation of the warmest quarter (BIO18) was the most important predictor, contributing more than 50% to the model. The optimal BIO18 was 500 mm, with habitat suitability decreased at both higher and lower values; survival probability approached zero when precipitation exceeded 2000 mm. Model results showed that temperature seasonality (BIO4) promoted the survival probability of \u003cem\u003eL. scutellare\u003c/em\u003e when below 900, but reduced survival when exceeding this threshold, indicating tolerance to relatively high winter\u0026ndash;summer differences but sensitivity to excessive variability. Elevation imposed additional constraints, with survival probability approaching zero above 3000 m. Compared with \u003cem\u003eL. deliense\u003c/em\u003e, \u003cem\u003eL. scutellare\u003c/em\u003e demonstrated broader tolerance to warmest-quarter temperatures (BIO10), though its maximum survival probability was lower. Precipitation seasonality (BIO15) was also influential, with optimal survival occurring within the range of 40\u0026ndash;120; values outside this range suppressed survival (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe potential distribution of vectors of Scrub typhus under different climate scenarios\u003c/h3\u003e\n\u003cp\u003eUnder the historical climate scenarios, the total potentially suitable distribution area for \u003cem\u003eL. deliense\u003c/em\u003e is estimated at 31.97\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e, of which approximately 3.67\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e is classified as highly suitable area. Suitable areas for \u003cem\u003eL. deliense\u003c/em\u003e are mainly located in south of the Yangtze River in China, Southeast Asia, South Asia, and a few parts of North Australia. The high suitable area is primarily located in the Southeast China Coast, with a few parts of Southeast Asia (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). In addition, the total potentially suitable distribution area for \u003cem\u003eL. scutellare\u003c/em\u003e is estimated at 11.01\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e, of which approximately 1.84\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e is classified as highly suitable area. Suitable areas for \u003cem\u003eL. scutellare\u003c/em\u003e are primarily concentrated in temperate regions of southern and eastern coastal areas of China, Honshu Island of Japan, and southern South Korea (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eUnder the different future climate scenarios (ssp1-2.6 and ssp5-5.8), the suitable areas for both \u003cem\u003eL. delicense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e showed expansion trends (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For the same time period, the suitable area under the SSP5-8.5 scenario was consistently larger than that under the SSP1-2.6 scenario. Furthermore, the suitable areas gradually increased over time. The maximum total suitable habitat areas for \u003cem\u003eL. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e were projected to be 59.01\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e and 27.36\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e respectively, representing increases of 84.58% and 148.50% compared to their historical climate scenario distributions.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSuitable areas for vectors of Scrub typhus across the world under different climatic scenarios (\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eClimate Scenarios\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePeriod\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eLess suitability\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eModerately suitability\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eHigh suitability\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eTotal area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eArea change\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eArea change Rate (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eL. deliense\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eHistorical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1970\u0026ndash;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003essp1-2.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2021\u0026ndash;2040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e15.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e47.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e16.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e51.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003essp5-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2021\u0026ndash;2040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e15.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e48.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e59.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e27.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e84.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eL. scutellare\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eHistorical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e1970\u0026ndash;2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003essp1-2.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2021\u0026ndash;2040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e9.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e83.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e10.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e97.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003essp5-8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2021\u0026ndash;2040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e89.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e16.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e148.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically elucidates the global distribution patterns and climatic drivers of the two major scrub typhus vectors - \u003cem\u003eLeptotrombidium deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eRegarding spatial distribution characteristics, while both mite species are predominantly distributed in Asia, ecological niche modeling predicts potential suitable habitats across all continents except Antarctica. This distribution pattern demonstrates significant interspecific niche differentiation: \u003cem\u003eL. deliense\u003c/em\u003e primarily adapts to tropical climate zones, whereas\u003cem\u003e\u0026nbsp;L. scutellare\u003c/em\u003e shows preference for temperate environments. This divergence reflects their distinct climate adaptation strategies developed through long-term evolution.\u003c/p\u003e\n\u003cp\u003eIn terms of climatic driving mechanisms, precipitation was identified as the key factor influencing their distributions. For \u003cem\u003eL. deliense\u003c/em\u003e, the combined contribution of precipitation in the warmest and wettest quarters exceeds 60% [38], indicating strict dependence on summer rainfall. The species achieves peak survival probability in tropical monsoon climate zones (e.g., Southeast Asian rice paddies) with annual precipitation exceeding 1000 mm and even seasonal distribution [10]. This ecological requirement is further supported by the newborn larvae's specific preference for warm (28-30°C) and humid (85-90% RH) microhabitats [39]. In contrast, the distribution of \u003cem\u003eL. scutellare\u003c/em\u003e is mainly regulated by altitude [40], typically found in high-altitude mountainous areas characterized by low temperature and precipitation, demonstrating greater environmental tolerance.\u003c/p\u003e\n\u003cp\u003eRegarding ecological adaptation mechanisms, the study reveals both commonalities and interspecific differences. Both species reach maximum population density under moderate precipitation levels, which correlates closely with their biological traits. On one hand, Yang's (1992) research confirmed the aquatic adaptability of \u003cem\u003eL. scutellare\u003c/em\u003e larvae [41]; on the other hand, Rose et al.(2001) demonstrated that extreme precipitation can destroy mite egg chambers or disrupt ground microhabitats [42], collectively forming the \"intermediate precipitation optimum\" phenomenon. Simultaneously, interspecific differences are evident: \u003cem\u003eL. deliense\u003c/em\u003e strictly depends on regular monsoon precipitation (BIO16=900-4000mm), while \u003cem\u003eL. scutellare\u003c/em\u003e can tolerate broader precipitation variability (BIO15=40-120), reflecting typical adaptation strategies of tropical versus temperate species.\u003c/p\u003e\n\u003cp\u003eThese precipitation-mediated distribution patterns are further modulated by the synergistic effects of temperature seasonality and altitudinal gradients, ultimately forming well-defined biogeographic distribution patterns. This study not only deepens our understanding of the ecological adaptation mechanisms of vector mites but, more importantly, provides a scientific basis for predicting scrub typhus transmission risks under climate change scenarios, offering significant guidance for developing targeted prevention and control strategies. Early identification and elimination of \u003cem\u003eLeptotrombidium\u003c/em\u003e mite breeding sites (such as stagnant water bodies) should be carried out through measures like larviciding, drainage, or habitat modification. For high-risk groups such as farmers working in the field, personal protective equipment (PPE) should be promoted [43], and public health education should be conducted to raise awareness. Improving the knowledge, attitudes, and practices of healthcare workers and residents regarding scrub typhus prevention and control can help prevent outbreaks and epidemics to some extent, thus alleviating the burden of scrub typhus[44].\u003c/p\u003e\n\u003cp\u003eThe suitable habitats of the two vectors are projected to expand in future periods, which may be associated with global warming. As the climate continues to warm, mite habitats are likely to gradually extend toward colder polar regions, thereby further enlarging the endemic areas of scrub typhus and posing ongoing threats to public health [45]. Governments should pay close attention to greenhouse gas emissions in order to prevent the further expansion of zoonotic disease-suitable areas.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Vector survival is also affected by ecological factors such as vegetation cover, soil moisture, and host density, which were not included in the model. The occurrence data may be incomplete because of the limited scope of the literature search, which could influence the accuracy of the predictions. In addition,\u003cem\u003e\u0026nbsp;L. deliense\u003c/em\u003e and \u003cem\u003eL. scutellare\u003c/em\u003e are not the only vectors of scrub typhus, and future research should examine the global distribution of other important mite species.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that the two vectors of scrub typhus are suitable for survival on all continents except Antarctica. \u003cem\u003eL. deliense\u003c/em\u003e is more adapted to tropical regions, whereas \u003cem\u003eL. scutellare\u003c/em\u003e is better suited to temperate regions. Under future climate conditions, the suitable ranges of both vectors are expected to expand significantly. With ongoing climate change and global economic integration, the likelihood of scrub typhus vectors being introduced into more countries will increase. This indicates the need for early prediction and warning of vector invasion risks.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article\u0026nbsp;and its supplementary information files.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Vector Surveillance and Control Project (No. 102393220020020000012).\u003c/p\u003e\n\u003cp\u003eAuthors’ contributions\u003c/p\u003e\n\u003cp\u003eAuthor Contributions Conceptualization: Xinning Hao, Qiyong Liu.\u003c/p\u003e\n\u003cp\u003eData curation: Xinning Hao, Lianfang Feng, Qiyong Liu.\u003c/p\u003e\n\u003cp\u003eFormal analysis: Xinning Hao.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Qiyong Liu.\u003c/p\u003e\n\u003cp\u003eMethodology: Xinning Hao.\u003c/p\u003e\n\u003cp\u003eSoftware: Xinning Hao, Lianfang Feng, Zihang Wang.\u003c/p\u003e\n\u003cp\u003eSupervision: Qiyong Liu, Haoqiang Ji.\u003c/p\u003e\n\u003cp\u003eValidation: Qiyong Liu,\u0026nbsp;Lianfang Feng, Qiyong Liu.\u003c/p\u003e\n\u003cp\u003eVisualization: Xinning Hao.\u003c/p\u003e\n\u003cp\u003eWriting –original draft: Xinning Hao.\u003c/p\u003e\n\u003cp\u003eWriting –review \u0026amp; editing: Xinning Hao, Lianfang Feng, Zihang Wang, Haoqiang Ji, Shengping Dou,\u0026nbsp;Ning Zhao, Qiyong Liu.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the groups of ArcGIS, MaxEnt, GBIF and WorldClim for their contributions to the completion of this study.\u003c/p\u003e\n\u003cp\u003eAuthors' information\u003c/p\u003e\n\u003cp\u003eXinning Hao is a postgraduate student in the Department of Vector Biology and Control, School of Public Health, Shandong University. She is currently undertaking an internship at the National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, and has participated in several research projects on vectors and climate change.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMahajan SK. Scrub typhus. J Assoc Physicians India. 2005;53(11):954\u0026ndash;958.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlsen SJ, Campbell AP, Supawat K, et al. Infectious causes of encephalitis and meningoencephalitis in Thailand, 2003\u0026ndash;2005. Emerg Infect Dis. 2015;21(2):280\u0026ndash;287.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi W, Li GC, Liu XB, Liu QY, Lu L. Research progress in epidemiological characteristics and influencing factors of scrub typhus. Chin J Vector Biol Control. 2020;31(6):738\u0026ndash;743.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu G, Walker DA-O, Jupiter D, Melby PC, Arcari CM. A review of the global epidemiology of scrub typhus. PLoS Negl Trop Dis. 2017;11(11):e0006062. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pntd.0006062\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0006062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen J, Sun J, Wang Z, Ling F, Shi X, Zhang R, et al. Re-emergence of scrub typhus in Zhejiang Province, southern China: a 45-year population-based surveillance study. Travel Med Infect Dis. 2019;32:101495. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tmaid.2019.101495\u003c/span\u003e\u003cspan address=\"10.1016/j.tmaid.2019.101495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElders PND, Penna da Silva L, Parquet T, et al. Diagnostic accuracy of an in-house Scrub Typhus enzyme linked immunoassay for the detection of IgM and IgG antibodies in Laos. PLoS Negl Trop Dis. 2020;14(12):e0008858.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Yang JJ, Zhong YP, et al. Research progress on special clinical features and clinical diagnosis and treatment of ascariasis. Chin J Infect Dis. 2021;39(12):707\u0026ndash;710. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cmaj.cn311365-20210222-00065\u003c/span\u003e\u003cspan address=\"10.3760/cmaj.cn311365-20210222-00065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi GC, Liu QY. Epidemiological status of ascariasis. Dis Surveill. 2018;33(2):129\u0026ndash;138. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3784/i.issn.1003-9961.2018.02.006\u003c/span\u003e\u003cspan address=\"10.3784/i.issn.1003-9961.2018.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Q, Xu W, Lu S, et al. Landscape of emerging and re-emerging infectious diseases in China: impact of ecology, climate, and behavior. Front Med. 2018;12:3\u0026ndash;22. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11684-018-0613-5\u003c/span\u003e\u003cspan address=\"10.1007/s11684-018-0613-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSponchiado J, Melo G, Martins T, Krawczak F, Jacinavicius FC, Labruna M, et al. Ectoparasites of small mammals: determinants of community structure in South American savannah. Parasitology. 2017;144(4):475\u0026ndash;483.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa T, Hao M, Chen S, Ding F. The current and future risk of spread of Leptotrombidium deliense and Leptotrombidium scutellare in mainland China. Sci Total Environ. 2022;843:156986. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.scitotenv.2022.156986\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.156986\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng PY, Han TW, Wang JX, et al. Species diversity of ectoparasitic chigger mites (Acari: Prostigmata) on small mammals in Yunnan Province, China. Parasitol Res. 2016;115(9):3605\u0026ndash;3618. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00436-016-5097-7\u003c/span\u003e\u003cspan address=\"10.1007/s00436-016-5097-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhasomkusolsil S, Tanskul P, Ratanatham S, Watcharapichat P, Phulsuksombati D, Frances SP, et al. Influence of Orientia tsutsugamushi infection on the developmental biology of Leptotrombidium imphalum and L. chiangraiensis (Acari: Trombiculidae). J Med Entomol. 2014;49(6):1270\u0026ndash;1275.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLv Y, et al. Infestation and seasonal fluctuation of chigger mites on the Southeast Asian house rat (Rattus brunneusculus) in southern Yunnan Province, China. Int J Parasitol Parasites Wildl. 2021;14:141\u0026ndash;149. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijppaw.2021.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ijppaw.2021.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuzuki T. Studies on the bionomics and chemical control of tsutsugamushi (scrub typhus mites). II. Trombicula scutellaris Nagayo et al. in southern Kanto of Japan. Jpn J Exp Med. 1954;24(4):181\u0026ndash;197.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeng, M.L., et al., 2014. Advances in the study of Leptorombidium deliense in China. J. Pathog. Biol. 9 (08), 763\u0026ndash;765\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu YX, et al. Clinical characteristics of the autumn\u0026ndash;winter type scrub typhus cases in south of Shandong province, northern China. BMC Infect Dis. 2009;9:82. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2334-9-82\u003c/span\u003e\u003cspan address=\"10.1186/1471-2334-9-82\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao M, et al. Spring scrub typhus, People\u0026rsquo;s Republic of China. Emerg Infect Dis. 2006;12(9):1361\u0026ndash;1363. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3201/eid1209.051465\u003c/span\u003e\u003cspan address=\"10.3201/eid1209.051465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Modell. 2006;190(3\u0026ndash;4):231\u0026ndash;259. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecolmodel.2005.03.026\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2005.03.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarber RA, Gillingham PK, Hannah L, et al. Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Divers Distrib. 2022;28(1):128\u0026ndash;141. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ddi.13356\u003c/span\u003e\u003cspan address=\"10.1111/ddi.13356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePearson RG, Raxworthy CJ, Nakamura M, Peterson AT. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr. 2007;34(1):102\u0026ndash;117. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2699.2006.01594.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2699.2006.01594.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu T, Perrings C, Kinzig A, Collins JP, Minteer BA, Daszak P. Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: a review. Ambio. 2017;46(1):18\u0026ndash;29. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13280-016-0809-2\u003c/span\u003e\u003cspan address=\"10.1007/s13280-016-0809-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi JC. Research on vectors and pathogens of chigger mites in China. Guangzhou: Guangdong Science \u0026amp; Technology Press; 1997:1\u0026ndash;570.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhan HY, Xu Z, He YL. Epidemiological characteristics and influencing factors of scrub typhus in Jingjiang City of Jiangsu Province, 2011\u0026ndash;2015. Chin J Dis Control Prev. 2017;21(11):1119\u0026ndash;1122. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.16462/j.cnki.zhjbkz.2017.11.010\u003c/span\u003e\u003cspan address=\"10.16462/j.cnki.zhjbkz.2017.11.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou SH, Han TW, Wang JX, et al. Investigation of chigger mite species diversity parasitizing wild rodents in some areas of Fujian Province. Chin J Zoonoses. 2021;37(6):511\u0026ndash;519. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3969/j.issn.1002-2694.2021.00.076\u003c/span\u003e\u003cspan address=\"10.3969/j.issn.1002-2694.2021.00.076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou SX, Guo XG, Men XY, et al. A preliminary checklist of chigger mites in Yunnan Province, China. Acta Arachnol Sin. 2006;15(2):114\u0026ndash;122. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3969/j.issn.1005-9628.2006.02.012\u003c/span\u003e\u003cspan address=\"10.3969/j.issn.1005-9628.2006.02.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Zhao L, Zhang Z, et al. Detection of a novel Rickettsia from Leptotrombidium scutellare mites (Acari: Trombiculidae) from Shandong, China. J Med Entomol. 2017;54(3):544\u0026ndash;549. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jme/tjw234\u003c/span\u003e\u003cspan address=\"10.1093/jme/tjw234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao L, Zhu H, Zhang LS, et al. Ectoparasitic mites and Orientia tsutsugamushi infection in wild rodents in Yingshang County, Anhui Province. Chin J Pathog Biol. 2022;17(9):1061\u0026ndash;1063,1076. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13350/j.cjpb.22091\u003c/span\u003e\u003cspan address=\"10.13350/j.cjpb.22091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu XC, Cai XY, Lin C, et al. Population and chigger mite carriage status of wild rodents in Pudong New Area, Shanghai. Shanghai J Prev Med. 2023;35(12):1218\u0026ndash;1222. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19428/j.cnki.sjpm.2023.23209\u003c/span\u003e\u003cspan address=\"10.19428/j.cnki.sjpm.2023.23209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung LH, Wu WJ, Kuo CC, Wang HC. A checklist of chigger mites (Acari: Trombiculidae and Leeuwenhoekiidae) from Taiwan, with descriptions of three new species. J Med Entomol. 2015;52(6):1241\u0026ndash;1253.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing F, Jiang WL, Guo XG, et al. Infestation and related ecology of chigger mites on the Asian house rat (Rattus tanezumi) in Yunnan Province, Southwest China. Korean J Parasitol. 2021;59(4):377\u0026ndash;392. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3347/kjp.2021.59.4.377\u003c/span\u003e\u003cspan address=\"10.3347/kjp.2021.59.4.377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo Y, Zhou JX, Guo XG, et al. Species diversity and related ecology of chiggers on small mammals in a unique geographical area of Yunnan Province, Southwest China. Exp Appl Acarol. 2023;91:439\u0026ndash;461. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10493-023-00841-z\u003c/span\u003e\u003cspan address=\"10.1007/s10493-023-00841-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu ES. Analysis of current epidemiological characteristics of scrub typhus in China. Chin J Epidemiol. 1997;18(1):56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei L, Wang XW, Wang CM, et al. Ecological survey and analysis of small mammals and ectoparasites in six regions of Sichuan Province. Chin J Vector Biol Control. 2014;25(4):309\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhan YZ, Guo XG, Zuo XH, et al. Distribution of Leptotrombidium spp. in parts of Yunnan Province, China. Chin J Epidemiol. 2011;32(1):13\u0026ndash;16. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.issn.0254-6450.2011.01.004\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.issn.0254-6450.2011.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi GC, Wang YJ, Yue YJ, et al. Delineation of summer- and autumn-type scrub typhus endemic areas in China. Chin J Vector Biol Control. 2019;30(3):233\u0026ndash;236.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao H, Dai CW, Wang J. Host distribution and infection status of scrub typhus in Nanshan District, Shenzhen City. Jiangsu Prev Med. 2016;27(4):496\u0026ndash;497.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLv Y, Guo XG, Jin DC. Research Progress on Leptotrombidium deliense. Korean J Parasitol. 2018;56(4):313\u0026ndash;324. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3347/kjp.2018.56.4.313\u003c/span\u003e\u003cspan address=\"10.3347/kjp.2018.56.4.313\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarma RN, Mahadevan B. Laboratory colonisation of the scrub ty-phus vector, Leptotrombidium deliense (L.) and some observations on its life history and habits. Indian J Entomol 1974; 34: 300\u0026ndash;306.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhan YZ, Guo XG, Zuo XH, Wang QH, Wu Q. Research on the area distribution and host selection of Leptotrombidium scutellarein 19 counties of Yunnan Province. Chin J Parasitol Parasitic Dis 2011; 29: 393\u0026ndash;396 (in Chinese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang GR. Observation on the vitality of Leptorombidium scutellarelarvae in water. Chin J Vector Bio Control 1992; 6: 395 (in Chi-nese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRose, et al., 2001. Climate variability and change in the United States: potential impacts on water- and foodborne diseases caused by microbiologic agents. Environ. Health Perspect. 109 (2), 211\u0026ndash;221.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Yang XQ, Ming XL, et al. The epidemic situation and prevention and control status of scrub typhus. Chin J Zoonoses. 2023;39(10):1024\u0026ndash;1031.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie XF, Wang GY, Huang Y, et al. Advances in clinical research and epidemiology of scrub typhus in China (2010\u0026ndash;2020). Hainan Med J. 2023;29(19):1505\u0026ndash;1509.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa DL, Lun XC, Li C, Zhou RB, Zhao Z, Wang J, et al. Predicting the Potential Global Distribution of Amblyomma americanum (Acari: Ixodidae) under Near Current and Future Climatic Conditions, Using the Maximum Entropy Model. Biology-Basel. 2021;10(10):16. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biology10101057\u003c/span\u003e\u003cspan address=\"10.3390/biology10101057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: WOS:000716309100001\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasitology-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pare","sideBox":"Learn more about [Parasitology Research](http://link.springer.com/journal/436)","snPcode":"436","submissionUrl":"https://submission.nature.com/new-submission/436/3","title":"Parasitology Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Scrub typhus, Mite-borne disease, Maximum Entropy Model, Global distribution, Climate change, Leptotrombidium","lastPublishedDoi":"10.21203/rs.3.rs-7592864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7592864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Scrub typhus is a mite-borne disease caused by \u003cem\u003eOrientia tsutsugamushi \u003c/em\u003e(Ot), primarily transmitted by the chigger mites \u003cem\u003eLeptotrombidium deliense\u003c/em\u003e (\u003cem\u003eL. deliense\u003c/em\u003e) and \u003cem\u003eLeptotrombidium scutellare\u003c/em\u003e (\u003cem\u003eL. scutellare\u003c/em\u003e). To effectively control the transmission of this disease, it is essential to clarify the spatial distribution of suitable habitats for its vector species. This research focuses on estimating the global potential distribution of \u003cem\u003eLeptotrombidium deliense \u003c/em\u003eand \u003cem\u003eLeptotrombidium scutellare\u003c/em\u003e under both current and projected future climate conditions. Additionally, the study aims to evaluate the influence of key climatic variables on the geographical patterns of these two vectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Data on species distribution were obtained from the Global Biodiversity Information Facility (GBIF) and from publicly published literature in databases such as Web of Science and PubMed. The environmental variables were downloaded from WorldClim Global Climate Database. The Maximum Entropy Model was used to evaluate the contribution of elevation, slope, aspect and nineteen bioclimatic variables to vector survival, as well as to predict the suitable area for the vectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e It indicated that \u003cem\u003eL. deliense\u003c/em\u003e predominantly occurs in southern China, India, Australia, and Southeast Asian regions. In contrast, \u003cem\u003eL. scutellare\u003c/em\u003e is largely restricted to southern and eastern coastal areas of China. \u003cem\u003eL. deliense\u003c/em\u003e displays greater adaptability to tropical zones, whereas \u003cem\u003eL. scutellare\u003c/em\u003e shows higher survival potential in temperate zones. Specifically, \u003cem\u003eL. deliense\u003c/em\u003e demonstrates significant sensitivity to precipitation during both the warmest and wettest quarters, indicating a ecological preference for hot and humid tropical environments, while\u003cem\u003e L. scutellare \u003c/em\u003eis more sensitive to precipitation of warmest quarter. Under the SSP5-8.5 scenario, the suitable habitat areas for the two species are expected to increase by 84.58% and 148.50%, respectively, compared to historical levels. Climate change will have a significant impact on the expansion of suitable habitats for these species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e To effectively prevent disease outbreaks and their spread, it is recommended to further strengthen disease surveillance and control measures in highly suitable areas where vector organisms are already present. Simultaneously, entry-exit quarantine and vector invasion monitoring capabilities should be enhanced in other highly suitable regions to prevent the introduction and establishment of foreign vector species.\u003c/p\u003e","manuscriptTitle":"Predicting the Global Potential Distribution of Tow Major Vectors of Scrub Typhus Under Future Climate Scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 13:59:38","doi":"10.21203/rs.3.rs-7592864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T06:20:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T02:53:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T10:43:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274042790187516145575961990405641428920","date":"2025-09-16T15:20:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294640604012245397534806528246177384258","date":"2025-09-14T13:24:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-14T03:09:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T07:54:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T06:06:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasitology Research","date":"2025-09-11T14:12:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasitology-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pare","sideBox":"Learn more about [Parasitology Research](http://link.springer.com/journal/436)","snPcode":"436","submissionUrl":"https://submission.nature.com/new-submission/436/3","title":"Parasitology Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4f6c1292-c4b1-4ce5-80fa-a45c6f0997bc","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:12:39+00:00","versionOfRecord":{"articleIdentity":"rs-7592864","link":"https://doi.org/10.1007/s00436-025-08602-0","journal":{"identity":"parasitology-research","isVorOnly":false,"title":"Parasitology Research"},"publishedOn":"2025-12-09 15:59:07","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-09-22 13:59:38","video":"","vorDoi":"10.1007/s00436-025-08602-0","vorDoiUrl":"https://doi.org/10.1007/s00436-025-08602-0","workflowStages":[]},"version":"v1","identity":"rs-7592864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7592864","identity":"rs-7592864","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}