Diversity and spatiotemporal atlas of ticks in the Beijing-Tianjin-Hebei urban agglomeration based on MaxEnt model

Diversity and spatiotemporal atlas of ticks in the Beijing-Tianjin-Hebei urban agglomeration based on MaxEnt model | 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 Diversity and spatiotemporal atlas of ticks in the Beijing-Tianjin-Hebei urban agglomeration based on MaxEnt model Lingling Chen, Wanying Gao, Yang Song, Jiaqi Nie, Zengliang Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9251988/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This integrative assessment delineates present and projected hotspots for four dominant tick species in the Beijing-Tianjin-Hebei (BTH) region, providing a spatial basis for targeted surveillance and control of tick-borne diseases. Methods We conducted a comprehensive review of the latest literature to determine the current distribution of ticks in the BTH region. Subsequently, the MaxEnt model was used to analyze the climate and environmental factors that affect the distribution of dominant ticks, and simulated the spatio-temporal distribution characteristics of ticks in the BTH region in the future. Results Totally 19 tick species from 6 genera in the BTH region, including four dominant tick species such as Haemaphysalis longicornis , Haemaphysalis concinna , Dermacentor silvarum and Ixodes persulcatus . Hae. longicornis exhibited a widespread distribution, while Hae. concinna , D. silvarum , and I. persulcatus were predominantly found in the northern and northwestern parts of BTH region. The main environmental variables affecting their distributions were temperature (Bio11), elevation and normalized difference vegetation index (NDVI). The model predictions indicated that the suitable habitats of all four dominant species would experience varying degrees of fluctuation under future climate conditions. Specifically, during 2081–2100, the centroid of suitable habitats for Hae. longicornis , D. silvarum , and I. persulcatus is predicted to shift northwestward, while Hae. concinna is expected to shift northeastward. Conclusions In this study, we provided a comprehensive assessment of tick species composition and spatial distribution patterns in the BTH region, which could provide a valuable reference for future research on tick distribution and the surveillance of tick-borne diseases in the region. Ticks and tick-borne diseases MaxEnt model Beijing-Tianjin-Hebei (BTH) region Suitability area Environmental factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Ticks are obligate hematophagous ectoparasites that infest domestic animals, wildlife, and humans, and have been considered the second most important vectors of human diseases worldwide [ 1 ]. In China, 124 known tick species had been recorded, comprising 113 hard ticks (Ixodidae) and 11 soft ticks (Argasidae), which revealed substantial heterogeneity in tick diversity across the country [ 2 ]. In addition to being major ectoparasites of animals, ticks served as vectors and reservoirs for numerous zoonotic pathogens, including bacteria, viruses, and protozoa, with increasing diversity over the past 30 years [ 3 – 5 ] and posing serious threats to both human and animal health [ 6 – 8 ]. Climate change could indirectly affect the altering vegetation distribution, host abundance, human behavior, and land use, all of which influenced tick density [ 9 – 11 ]. Therefore, understanding the spatial distribution of dominant tick species under current and future climatic conditions is critical for effective prevention and control of tick-borne diseases. The various models had been developed to predict species distributions, including the Maximum Entropy (MaxEnt) model, Classification and Regression Trees (CART), Generalized Linear Models (GLMs), Habitat models (HABITAT), Genetic Algorithm for Rule-set Prediction (GARP), and Bioclimatic models (BIOCLIM) [ 12 , 13 ]. Among these, MaxEnt has become one of the most widely used due to its solid theoretical foundation in ecological niche modeling. Based on the principle of maximum entropy, the model estimated a species' potential habitat distribution from occurrence data and environmental variables, providing the least biased prediction that remained consistent with known information, even with limited data. The MaxEnt model was known for its high predictive accuracy, stability, and ease of interpretation, particularly when applied to species with sparse or discontinuous records [ 14 , 15 ], making it especially suitable for modeling the distribution of ticks and other vector species. The BTH region, located in northern China, was the country’s largest urban agglomeration in this area and features complex topography and ecosystems, including plains, hills, and mountains. These diverse habitats, along with rich animal resources, provided ideal breeding grounds and refuges for tick populations. Several tick-borne diseases, including spotted fever group rickettsiosis and severe fever with thrombocytopenia syndrome (SFTS), had been documented in this region [ 16 – 18 ]. Recent advances in microbiome research had further highlighted the complexity of pathogen dynamics. For instance, a global longitudinal analysis of Campylobacter spp. antimicrobial resistance provided critical context for understanding zoonotic threats [ 19 ], while novel bioinformatic tools now enabled the detection of microbial biosynthetic gene clusters, offering new avenues for exploring pathogen-host interactions [ 20 ]. In recent years, the BTH region has undergone rapid urbanization and population growth while also implementing strict environmental protection policies. This dual process has expanded the extent of human activity, increasing the likelihood of human exposure to ticks and tick-borne pathogens. Therefore, applying the MaxEnt model in conjunction with ArcGIS spatial analysis to investigate the distribution and future trends of dominant tick species in the BTH region is of great significance for public health surveillance and disease prevention. In this study, we provided a comprehensive overview of the spatial distribution of tick species in the BTH region of China. By integrating the MaxEnt model with ArcGIS spatial analysis, we predicted the potential suitable habitats and projected future distribution shifts of dominant tick species under both current and future climatic scenarios. These findings provide critical insights for regional tick-borne disease surveillance and risk assessment, establishing a scientific basis for the development of targeted prevention and control strategies. 2 Materials and Methods 2.1 Collection of tick geographical distribution data The BTH urban agglomeration is located in the northern part of the North China Plain and encompasses 13 cities, including Beijing, Tianjin, and 11 cities in Hebei Province. Data on tick distribution were collected through literature retrieval and data extraction. We searched major scientific citation databases, including PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ), Web of Science, China National Knowledge Infrastructure (CNKI), and the Wanfang Database, using the keywords: “tick,” “Beijing,” “Hebei,” and “Tianjin.” The search covered all relevant publications available up to July 25th, 2024. In addition, we retrieved occurrence records from the Global Biodiversity Information Facility (GBIF) ( https://www.gbif.org/ ) and the authoritative monograph Fauna Sinica: Arachnida: Ixodida from the Fauna of China series. Relevant distributional data and collection locations were extracted using the same procedures. We manually reviewed the titles, abstracts, and full texts of the articles and selected those that reported tick distribution in the BTH region. From these, we extracted geographic location and coordinate information. If longitude and latitude were not explicitly provided, we used the Baidu Map Coordinate Picker ( https://api.map.baidu.com/lbsapi/getpoint/index.html ) to determine approximate coordinates based on the described locations. 2.2 Selection of environmental variables Environmental variables used in this study included both bioclimatic and geographic factors. Nineteen bioclimatic variables (Bio1–Bio19) were obtained from the WorldClim database ( http://www.worldclim.org ) at a spatial resolution of 2.5 arc-minutes. To avoid model overfitting due to spatial autocorrelation among occurrence points, we used the SDMToolbox v2.5 plugin in ArcGIS to spatially rarefy the presence records. Pearson correlation analysis was then conducted in R to assess collinearity among variables. Variables with an absolute correlation coefficient |r| > 0.8 were considered collinear. Among these, variables with higher ecological contribution were retained. The geographic data, including elevation, slope, and aspect, were derived from the ASTER Global Digital Elevation Model (GDEM) ( https://gdemdl.aster.jspacesystems.or.jp/index_en.html ). Slope and aspect were calculated using ArcGIS 10.4. Normalized Difference Vegetation Index (NDVI) data were obtained from the MOD13A3 dataset ( https://www.earthdata.nasa.gov/ ). Vector data for provincial administrative boundaries in the BTH region were acquired from the National Geomatics Center of China ( https://www.tianditu.gov.cn/ ) under the cartographic license number GS (2024) 0650, with the coordinate system set to GCS_WGS_1984. 2.3 Construction of the MaxEnt model For the dominant tick species identified, the MaxEnt (Maximum Entropy) model was applied to analyze the key environmental factors influencing their distribution in the BTH region. A total of 75% of the occurrence records were randomly selected as the training dataset, while the remaining 25% were used for testing and validation of the model performance. The convergence threshold was set to 10 − ⁴, and the maximum number of iterations was set to 500. For each dominant species, ten bootstrap replicates were performed to ensure robustness of the predictions. The refined species occurrence data and environmental variables were treated as the input data by the MaxEnt software. The jackknife method was employed to evaluate the relative contribution of each environmental variable. Univariate response curves were constructed to determine the environmental suitability range for each species. The accuracy of the model prediction was assessed using the Receiver Operating Characteristic (ROC) curve, with the area under the curve (AUC) as the evaluation metric. AUC values range from 0 to 1, where values below 0.6 indicate poor predictive performance, 0.6–0.7 suggest low accuracy, 0.7–0.8 indicate moderate accuracy, 0.8–0.9 reflect good predictive ability, and values above 0.9 denote excellent performance. The closer the AUC value is to 1, the more accurate the prediction and the stronger the correlation between species distribution and environmental variables. With the ArcGIS 10.4, the mean results of the MaxEnt replicates were used to generate a habitat suitability index (HSI) ranging from 0 to 1. Higher values indicate more suitable habitats. The HSI values were classified into four suitability levels: high suitability, moderate suitability, low suitability, and unsuitable. MaxEnt modeling was conducted by using the MaxEnt software, geospatial analysis was performed in ArcGIS 10.8, and plotting was conducted in R v4.3.2. 2.4 Projection of future habitat suitability Future climate data were obtained from the BCC-CSM2.MR model, a high-resolution climate projection model developed under the Coupled Model Intercomparison Project Phase 6 (CMIP6). This model includes atmosphere, land surface, ocean and sea ice. The future scenario selected in this study was SSP245, an upgraded version of the medium-intensity SSP2 pathway, comparable to the RCP4.5 scenario. According to climate projections, RCP4.5 peaks around 2040 and stabilizes before 2080, which aligns with China's future development trajectory. Therefore, four future time periods were selected under the SSP245 scenario: 2021–2040, 2041–2060, 2061–2080, and 2081–2100. Environmental variables for each future period were combined with the current topographic and species occurrence data and re-entered into the MaxEnt model to simulate the potential distribution of dominant tick species in the BTH region under future climate scenarios. 2.5 Centroid shift of suitable habitats To evaluate spatial changes in suitable habitats over time, we analyzed the shifts in the geographic centroids of habitat suitability for each dominant tick species from the present day to the time period of 2081–2100. Binary transformation of the habitat suitability maps generated by MaxEnt was conducted using ArcGIS 10.4 to extract the coordinates of the most suitable regions. By linking the centroid coordinates across time periods, we identified both the direction and magnitude of habitat centroid shifts, revealing temporal trends in the geographic distribution of suitable habitats under climate change. 3 Results 3.1 Distribution of tick species in the Beijing-Tianjin-Hebei region A total of 1446 articles were retrieved, including 784 Chinese literature (462 from CNKI, 322 from Wanfang) and 662 foreign literatures (633 from PubMed, 29 from Web of Science). After removing duplicates and articles with unclear or irrelevant distribution information (data cleaning), 40 articles were screened for the final analysis, with 33 Chinese literature and 7 foreign literatures (Fig. S1 ). In addition, based on both our own field sampling records and occurrence data extracted from published literature, we compiled the distribution data of tick species in the BTH region. In total, 19 tick species belonging to 6 genera have been recorded to date across the BTH region, with distribution mapped at the prefecture level or below (Fig. 1 ). Hae. longicornis was found to be widely distributed in both plain and mountainous areas of the region, while other tick species were primarily recorded in the northern part of the BTH region. Based on the known distribution patterns of various tick species across different ecological habitats and the availability of occurrence data, we selected four dominant tick species in the region for subsequent ecological niche modeling analyses. A total of 167 occurrence records were compiled from 55 publications, with Hae. longicornis accounting for the majority (116 records from 30 publications), followed by D. silvarum (31 records from 12 publications), I. persulcatus (11 records from 10 publications), and Hae. concinna (9 records from 3 publications). 3.2 Environmental variables influence tick distribution To construct species-specific MaxEnt models for different tick species, an initial set of 19 bioclimatic variables was considered. The species-specific subsets of variables were then selected based on correlation analysis. The contributions of environmental variables were assessed by using the Jackknife test, with the key influencing variables for each species summarized (Table S1 ). Furthermore, to investigate the distribution characteristics of ticks, the environmental factors affecting the current and potential distribution of tick species had been analyzed (Fig. S2 A-D). The distribution of Hae. longicornis and Hae. concinna was mainly influenced by elevation, mean temperature of the coldest quarter (Bio11), slope, and NDVI. For D. silvarum , the key variables were elevation, slope, and NDVI. The distribution of I. persulcatus was primarily driven by Bio11 and aspect. The probability of occurrence for Hae. longicornis and D. silvarum reaches its maximum at elevations of 184.36 meters and 1180.58 meters, respectively, and at temperatures of -4.50°C and − 18.26°C. Furthermore, the regularized training gain results of the MaxEnt models, as shown in the Jackknife plots (Fig. S2 A-D), revealed that when using single environmental variables, elevation contributed the highest gain for Hae. longicornis , followed by Bio11, slope, Bio3, and Bio16. For Hae. concinna , Bio11 exhibited the highest gain, followed by NDVI, slope, and Bio17. For D. silvarum , elevation had the highest gain, followed by Bio11, slope, Bio3, and Bio16. For I. persulcatus , Bio11 showed the highest gain, followed by aspect and Bio3. Based on the results of the pearson correlation analysis, the final selected variables for modeling were as follows: for Hae. longicornis , ten variables were selected, including Bio2, Bio3, Bio11, Bio14, Bio15, Bio16, elevation, slope, aspect, and NDVI; for Hae. concinna , six variables were selected, including Bio3, Bio11, Bio17, slope, aspect, and NDVI; for D. silvarum , nine variables were selected, including Bio2, Bio3, Bio7, Bio12, Bio17, elevation, slope, aspect, and NDVI; and for Ixodes persulcatus , five variables were selected, including Bio3, Bio11, slope, aspect, and NDVI. 3.3 Suitable habitat for dominant tick species under current climate conditions Based on ten replicates of MaxEnt runs with 19 environmental variables, the average AUC values for Hae. longicornis , Hae. concinna , D. silvarum , and I. persulcatus were 0.88, 0.88, 0.91, and 0.86, respectively, indicating high predictive performance (Fig. S2 E-H). According to the AUC curve evaluation criteria, a higher AUC value for the test set indicates better model performance. In this study, the high AUC values obtained demonstrate that the predictive models performed well. Predicted suitable habitats for these species in the BTH region were classified into four categories: highly suitable, moderately suitable, low suitability, and unsuitable areas (Fig. 2 ). Under current climatic conditions, the distributions of the four tick species exhibit both spatial overlap and distinct patterns. Hae. longicornis shows a characteristic belt-shaped distribution, with areas of moderate to high suitability extending along the Taihang and Yanshan mountain ranges in a north-south orientation. In contrast, most of the southern plain areas of the BTH region are predicted to be of low or unsuitable habitat suitability for this species. For Hae. concinna , the high-suitability zones are mainly concentrated in the northern mountainous areas of Hebei province, including the Chengde, northern Tangshan, and Zhangjiakou regions, suggesting a strong ecological adaptation to local environmental conditions. The suitable habitat for Dermacentor silvarum was also predominantly located in the northern mountainous areas of the BTH region, particularly in the Zhangjiakou, Chengde, and northern Baoding regions of Hebei province, where high habitat suitability had been predicted. In contrast, the southern and southwestern parts of BTH were largely unsuitable or show low suitability for this species. Ixodes persulcatus was predicted to have suitable habitats mainly in the northern peripheral areas of BTH, particularly near the border between Hebei province and the BTH region, such as the northern part of Zhangjiakou and Chengde. Habitat suitability decreases substantially in the central and southern plain areas. Overall, the distribution patterns of these four tick species were clearly shaped by topography and climate, with suitable habitats primarily located in higher-elevation mountainous and hilly regions characterized by more moderate climatic conditions. The results here provided a valuable spatial framework to inform targeted surveillance and control of tick-borne diseases in the BTH region. 3.4 Prediction of spatial distributions for tick species under the coming decades The potential suitability areas for dominant tick species were predicted in the coming periods. During 2081–2100, the potentially suitable habitat for Hae. longicornis was projected to increase by 35,583 km². Areas of moderate and low suitability were each projected to expand by 8,266 km², primarily around Zhangjiakou, Chengde, and the peripheral areas of Beijing. In contrast, approximately 106,186 km², mostly in the southeastern plains, would become unsuitable. This species was expected to expand its range steadily throughout the 21st century. The persistent risk in western highlands and low-altitude valleys underscored the need for long-term monitoring and region-specific vector control strategies under climate change scenarios (Fig. 3 ). For Hae. concinna , the high-suitability zones would gradually shrink and became more fragmented, particularly in the northern areas. Although suitable regions persisted, their density decreased, likely due to habitat fragmentation or environmental constraints caused by climatic fluctuations (Fig. 4 ). The D. silvarum was projected to remain confined to the ecologically favorable northwestern regions, particularly the Zhangjiakou area of Hebei province. Its high-suitability area is expected to increase by 433 km² by 2081–2100, though inter-period fluctuations suggest climate variability could impact its spatial stability. Targeted surveillance in these high-risk areas is warranted (Fig. 5 ). The I. persulcatus is expected to remain largely restricted to northern and northwestern areas with cooler climates and dense vegetation. While central and southern plains remain unsuitable, the total suitable area may increase by 16,673 km² during the time period of 2081–2100 (Fig. 6 ). Only the moderately suitable area is projected to expand, while highly and low-suitability zones are expected to decline. These findings highlight the necessity of prioritizing monitoring efforts in the northern high-risk zones to enable early warning and control of I. persulcatus and its associated pathogens under changing climate conditions (Fig. 6 ). 3.5 Changes in centroid of the potential suitability areas in different future periods To assess the future spatial dynamics of dominant tick species, the changes in their potential habitat centroided across different time periods were further analyzed (Fig. 7 ). By 2081–2100, Hae. longicornis was projected to shift its potential suitability centroid approximately 57.6 km northwest, with a latitude increase of about 0.5° and a longitude change of 0.14°, indicating a trend of northward expansion. Similarly, Hae. concinna was expected to shift about 63.0 km to the northeast, with changes of approximately 0.4° in latitude and 0.5° in longitude, reflecting a northeastward expansion. The D. silvarum showed a projected centroid shift of around 71.1 km to the northwest, with a latitude change of 0.6° and a longitude change of 0.4°, suggesting a westward expansion of its suitable habitat. In contrast, I. persulcatus is expected to shift its centroid approximately 50.0 km northward, with a latitude change of 0.4° and only a minor change in longitude. However, its overall potential suitability area still demonstrated a westward expansion trend. These results indicated species-specific but consistent directional shifts under future climatic conditions. 4 Discussion In this study, based on tick distribution data from the BTH region, we provided a comprehensive summary of the distribution characteristics of tick species in the BTH area. By integrating climatic and environmental factors with ecological and geographical features, the MaxEnt model was employed to analyze the habitat suitability of dominant tick species and to predict their current and future potential spatial distributions. A total of 19 tick species from 6 genera were revealed in the BTH region, with Hae. longicornis , Hae. concinna , D. silvarum , and I. persulcatus being the dominant species. The suitable habitat ranges varied among species, with Hae. longicornis and D. silvarum exhibiting expanding trends. Model performance evaluation indicated high predictive reliability for the 4 dominant species, with AUC values of 0.88, 0.88, 0.91 and 0.86, respectively. The distribution of different tick species varied across regions. Hae. longicornis exhibited a wide distribution, spanning forested areas, mountainous regions, and plains. The expanding global distribution of Hae. longicornis , coupled with its capacity to carry a wide array of pathogens, positioned this species as an emerging vector of significant public health concern. Originally native to eastern and central Asia, Hae. longicornis had successfully invaded regions such as Australia, New Zealand, the Pacific Islands, and the United States, largely owing to its capacity for parthenogenetic reproduction, whereby a single female tick can establish a population [ 21 – 26 ]. Hae. longicornis is also an efficient vector capable of transmitting multiple viruses, including Powassan virus, Khasan virus, tick-borne encephalitis virus, and severe fever with thrombocytopenia syndrome virus (SFTSV), all of which are increasing in incidence and geographic range, posing serious threats to both human health and animal welfare [ 27 – 29 ]. As the predominant tick species in the BTH region, Hae. longicornis harbored a diverse array of medically and veterinary important pathogens, including Anaplasma phagocytophilum , Anaplasma ovis and Anaplasma capra [ 30 , 31 ]. Previous studies showed that Rhipicephalus microplus in the BTH region harbored multiple pathogens, including Rickettsia raoultii , Borrelia garinii , Babesia venatorum , A. ovis and A. phagocytophilum [ 31 , 32 ]. In contrast, Hae. concinna , D. silvarum , and I. persulcatus were primarily found in the grassland–forest ecotones of northern BTH area, such as Zhangjiakou and Chengde. The tick Hae. concinna was known to carry a range of pathogens, including R. raoultii and Babesia venatorum [ 30 , 31 ]. D. silvarum was capable of carrying a wide range of tick-borne pathogens, including tick-borne encephalitis virus, R. slovaca , R. raoultii , A. phagocytophilum , Babesia caballi , Theileria equi , as well as Ehrlichia chaffeensis , the causative agent of human monocytic ehrlichiosis [ 32 – 35 ]. Moreover, with the advancement of high-throughput sequencing technologies, several novel tick-borne viruses had been identified in D. silvarum in recent years, such as the Tacheng tick virus 1 (TcTV-1) and Jingmen tick virus (JMTV) [ 36 , 37 ]. The I. persulcatus was one of the most important vectors of zoonotic diseases in the Northern Hemisphere, with a wide distribution across the Eurasian continent [ 38 ]. It readily bit humans and was confirmed to transmit multiple microbial pathogens, including tick-borne encephalitis virus (TBEV), Anaplasma phagocytophilum , Babesia divergens , and Borrelia afzelii , all of which caused multi-organ and multi-system damage [ 38 – 40 ]. In addition, the newly identified tick-borne virus, Alongshan virus, discovered in 2019, had also been closely associated. Between 2009 and 2012, clinical cases in Hebei Province suspected of rickettsial infections were screened for emerging A. phagocytophilum and spotted fever group Rickettsiae (SFGR). The results revealed that 10.9% of the cases tested positive for emerging SFGR, while 8.9% were positive for emerging A. phagocytophilum , indicating the presence and public health relevance of these emerging tick-borne pathogens in the region [ 41 , 42 ]. These ecologically diverse and logistically active areas supported the development and spread of ticks and their pathogens. The current and future suitable habitats of the four dominant tick species were predicted under various ecological conditions using the MaxEnt model. The findings offered insights into the ecological characteristics and future risk landscapes of tick populations in the BTH region, providing a scientific basis for disease surveillance and early-warning systems. Analysis of crucial climatic and environmental variables revealed that Bio11, NDVI, and topography significantly influenced the distribution of Hae. longicornis , Hae. concinna , D. silvarum and I. persulcatus . Notably, their potential suitable habitats showed considerable overlap. The predicted distribution probabilities of Hae. longicornis and Hae. concinna peaked at elevations of 184.36 m and 1180.58 m, respectively, indicating inter-specific differences in altitude preferences among Haemaphysalis ticks. That suggested that the Hae. longicornis tended to inhabit lower elevations and had a broader ecological niche. Furthermore, the probability of occurrence for all four species peaked at temperatures of -4.50°C and − 18.26°C, demonstrating the strong temperature sensitivity of ticks [ 43 ]. Under current climatic conditions, the suitable habitat of Hae. longicornis encompassed approximately 33.85% of the total area of the BTH region, indicating a widespread distribution. From a broader temporal perspective, notable shifts in the potential suitable habitats of these four tick species were projected under future climatic scenarios (2081–2100). There were several studies that were consistent with our model. For instance, previous researches revealed that the northeastern forest region would become warmer and suitable for Dermacentor nuttalli due to global warming and land-use changes [ 43 ]. Besides, the studies predicted an expansion of suitable habitats for I. persulcatus in BTH by 2070 [ 44 ]. Thus, there was evident that the combined effects of climate change, human activities, land use, and vector population growth would lead to the expansion of suitable habitat areas for the dominant tick species in the BTH region. In line with this, this study showed that by 2081–2100, the habitat centers of Hae. longicornis , I. persulcatus , and D. silvarum would be expected to shift northwestward, with localized expansions in the Zhangjiakou and Chengde regions of Hebei province. In contrast, Hae. concinna was projected to exhibit a northeastward habitat shift, with new suitable areas emerging in the Chengde area of Hebei province. These distributional changes may be attributable to ongoing ecological restoration projects in the BTH region, including cropland-to-forest conversion, afforestation subsidies and forestry compensation schemes, which had significantly enhanced vegetation cover and mitigated desertification [ 45 ]. In this study, we analyzed the distribution patterns of tick species in the BTH region by using comprehensive literature and field-collected data. By integrating species occurrence records with key bioclimatic variables, we applied the MaxEnt model and spatial analysis tools to predict the current and future distributions of the dominant tick species under climate change scenarios. These findings underscored the dynamic nature of tick habitat suitability under changing environmental conditions and highlighted the potential impact of ecological engineering on the bio-geographic patterns of medically important tick species. This study represented the first integrated effort to systematically map tick species distributions in BTH region by using a combination of literature and field-collected data, highlighting current and projected distribution shifts of four medically important tick species. The identification of future high-risk areas would help guide disease surveillance and control strategies, optimize resource allocation, and reduce the risk of tick-borne disease outbreaks. Nonetheless, there were still some limitations in this study. First, tick distribution data were primarily derived from published literature, which may be incomplete or biased, potentially underestimating the true distribution range. To address sampling bias, we removed duplicate records from the same locations and applied minimum distance thresholds for sampling points. Second, the MaxEnt model predicted the potential habitat suitability, not the actual distribution or population density. This model only accounted for abiotic factors and did not incorporate biotic interactions such as host availability, which may explain the presence of ticks in areas predicted to be unsuitable. Host movement patterns, population dynamics, and human activity were not considered. Finally, the absence of long-term field validation data limits our ability to assess the accuracy of projected distribution shifts over time. Despite these limitations, this study provides the first systematic spatial assessment of dominant tick species in the BTH region and offers valuable baseline information for future surveillance and control strategies. 5 Conclusion In summary, in this study, we provided a systematic overview of tick diversity and distribution patterns in the BTH region based on consolidated regional data. By integrating the MaxEnt model with ArcGIS spatial analysis, we projected the potential suitable habitats of four dominant tick species under current and future climate scenarios. These findings offered a scientific foundation for advancing tick research and strengthening regional surveillance and control strategies for tick-borne diseases. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Data availability: Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing of Interest The authors declare no conflicts of interest Funding This research was supported by the Youth Fund of the Natural Science Foundation of Tianjin (24JCQNJC00460). Authors’ contributions Conceptualisation and methodology, S.L.J.; Software, W.Y.G.; Validation, L.L.C. and Z.L.W.; Formal analysis, L.L.C. and Y.S.; Investigation, L.L.C. and Z.L.W.; Data curation, J.Q.N., H.N.C. and X.J.; Writing—original draft preparation, L.L.C.; Writing—review and editing, W.Y.G., S.L.J. All authors have read and agreed to the published version of the manuscript. 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Longitudinal Trends and Drivers of Antimicrobial Resistance in Campylobacter Worldwide (1954–2023). Zoonoses. 2025;5:11. Yang G, Yu GH, Cui Q et al. BioMGCore: A toolkit for detection of biological metabolites in microbiome. iMetaOmics. 2025; 2(4). Beard CB, Occi J, Bonilla DL, et al. Multistate infestation with the exotic disease–vector tick Haemaphysalis longicornis -United States, august 2017–September 2018. Morb Mort Wkly Rep. 2018;67:1310–3. Tanne JH. New tick seen in nine US states is an emerging disease threat, warns CDC. BMJ. 2018;363:k5191. Rainey T, Occi JL, Robbins RG, et al. Discovery of Haemaphysalis longicornis (Ixodida: Ixodidae) parasitizing a sheep in New Jersey, United States. J Med Entomol. 2018;55:757–9. Wormser GP, McKenna D, Piedmonte N, et al. First recognized human bite in the United States by the Asian longhorned tick, Haemaphysalis longicornis . Clin Infect Dis. 2020;70:314–6. Zhao L, Li J, Cui X, et al. 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Investigation of tick-borne bacterial microorganisms in Haemaphysalis ticks from Hebei, Shandong, and Qinghai provinces, China. Ticks Tick Borne Dis. 2024;15(2):102290. Jiang J, Jiang B, Yu J, et al. Anaplasma phagocytophilum infection in ticks, China-Russia border. Emerg Infect Dis. 2011;17:932–4. Johnson N, Migné CV, Gonzalez G. Tick-borne encephalitis. Curr Opin Infect Dis. 2023;36(3):198–202. Tian Z, Liu G, Shen H, Xie J, Luo J, Tian M. First report on the occurrence of Rickettsia slovaca and Rickettsia raoultii in Dermacentor silvarum in China. Parasites Vectors. 2012;5:19. Wen B, Cao W, Pan H. Ehrlichiae and ehrlichial diseases in china. Ann N Y Acad Sci. 2003;990(1):45–53. Jia N, Liu H, Ni X, et al. Emergence of human infection with Jingmen tick virus in China: A retrospective study. EBioMedicine. 2019;43:317–24. Liu X, Zhang X, Wang Z, et al. A tentative tamdy orthonairovirus related to febrile illness in Northwestern China. Clin Infect Dis. 2020;70:2155–60. Pakanen VM, Sormunen JJ, Sippola E, et al. Questing abundance of adult taiga ticks Ixodes persulcatus and their Borrelia prevalence at the north-western part of their distribution. Parasit Vectors. 2020;13:384. Gray JS, Ogden NH. Ticks, human babesiosis and climate change. Pathogens. 2021;10:1430. Dugat T, Lagrée AC, Maillard R, et al. Opening the black box of Anaplasma phagocytophilum diversity: current situation and future perspectives. Front Cell Infect Microbiol. 2015;5:61. Xue J, Ren Q, Yang XL, et al. Human pathogens in ticks removed from humans in Hebei, China. Heliyon. 2023;9(3):e13859. Teng Z, Shi Y, Zhao N, et al. Molecular Detection of Tick-Borne Bacterial and Protozoan Pathogens in Haemaphysalis longicornis (Acari: Ixodidae) Ticks from Free-Ranging Domestic Sheep in Hebei Province, China. Pathogens. 2023;12(6):763. Yang X, Gao Z, Wang L, et al. Projecting the potential distribution of ticks in China under climate and land use change. Int J Parasitol. 2021;51(9):749–59. Ma R, Li C, Tian H, et al. The current distribution of tick species in Inner Mongolia and inferring potential suitability areas for dominant tick species based on the MaxEnt model. Parasit Vectors. 2023;16(1):286. Liang J, Zhong M, Zeng G, Chen G, Hua S, Li X, Yuan Y, Wu H, Gao X. Risk management for optimal land use planning integrating ecosystem services values: A case study in Changsha, Middle China. Sci Total Environ. 2017;579:1675–82. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Supplementaryfigure1.pdf Supplementaryfigure2.pdf GraphicalAbstract.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-9251988","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622853856,"identity":"d73f30af-79e7-42f3-b25f-be6b14ad6d5f","order_by":0,"name":"Lingling Chen","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingling","middleName":"","lastName":"Chen","suffix":""},{"id":622853857,"identity":"a84ad278-5bfb-412f-9df5-7f1f972c0cf8","order_by":1,"name":"Wanying Gao","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wanying","middleName":"","lastName":"Gao","suffix":""},{"id":622853858,"identity":"a8d8b10e-bbec-46df-9004-7db6b009cd3c","order_by":2,"name":"Yang Song","email":"","orcid":"","institution":"Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Song","suffix":""},{"id":622853859,"identity":"9c03da6e-766f-40df-ade9-6fc015c4d540","order_by":3,"name":"Jiaqi Nie","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Nie","suffix":""},{"id":622853860,"identity":"1311e15e-3d47-4503-bebb-0943a14d8894","order_by":4,"name":"Zengliang Wang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Zengliang","middleName":"","lastName":"Wang","suffix":""},{"id":622853861,"identity":"7badf6cc-ad45-4625-b6b5-bdb9ff040aaa","order_by":5,"name":"Henan Cao","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Henan","middleName":"","lastName":"Cao","suffix":""},{"id":622853862,"identity":"b95b687c-3c20-4e00-87e5-641cb05126bd","order_by":6,"name":"Xiao Jiang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Jiang","suffix":""},{"id":622853863,"identity":"fecb724c-e895-4577-86c9-0749292b3e84","order_by":7,"name":"Shulei Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie2PsWrDQAxAFQS6RcGri4vzCwoG0yEfYy+eSmkIZAokYLguha7NXwT6AzWi7ta5e37A4KVDIbmsGXweM9wbNBzvoRNAIHCDTGoABNikZAA+O1mkY5M2i9xs3p+rbNQmJ2O5v7TcaenXX/DYLy1NDoqNLgQLMPp1GP4Y5cne3qMoFfoo9ARcVb+eW3KcWiJRFpfwCmLOPYnp+6lF50edPkhc7vwJS+KS+K5mUBAZlawT/mklQpLmVYqMfLfM374/el5vttbUx+7v/5RGRtvhZHf9QkP6hZlPCAQCgQCcAWwkQ6gbqwOjAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shulei","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2026-03-28 10:39:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9251988/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9251988/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107424404,"identity":"5504cd6d-a64d-44e7-9e2b-b244818c2cfd","added_by":"auto","created_at":"2026-04-21 10:57:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62626,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of tick species in the BTH region. Different colors represent different tick species.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/77184959d2b458f1723cfb64.jpeg"},{"id":107489629,"identity":"1c648e67-97f2-4c41-9ede-3858bbd5ba41","added_by":"auto","created_at":"2026-04-22 02:48:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64509,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted spatial distribution of tick suitability areas under the current climate scenario in BTH areas. (A) \u003cem\u003eHae. longicornis\u003c/em\u003e; (B)\u003cem\u003eHae. concinna\u003c/em\u003e; (C)\u003cem\u003e D. silvarum\u003c/em\u003e; (D)\u003cem\u003e I. persulcatus\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/04dd930837a2af56b6251d08.jpeg"},{"id":107424478,"identity":"b726e1a9-1a62-4cb2-a3ab-aa96f2083f63","added_by":"auto","created_at":"2026-04-21 10:57:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59800,"visible":true,"origin":"","legend":"\u003cp\u003eProjected changes in the spatial distribution of suitable habitats for \u003cem\u003eHae. longicornis\u003c/em\u003e in the BTH Region. (A) 2021–2040; (B) 2041–2060; (C) 2061–2080; (D) 2081–2100.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/3bc785a4fb6c215b167a1fdb.jpeg"},{"id":107424356,"identity":"17cc1a53-7a98-4b00-ae75-590bb8c6974d","added_by":"auto","created_at":"2026-04-21 10:57:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46769,"visible":true,"origin":"","legend":"\u003cp\u003eProjected changes in the spatial distribution of suitable habitats for \u003cem\u003eHae. concinna\u003c/em\u003e in the BTH Region. (A) 2021–2040; (B) 2041–2060; (C) 2061–2080; (D) 2081–2100.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/99adab25408ca94599c1fe16.jpeg"},{"id":107424407,"identity":"dc54cb98-3087-4c0b-b825-7cd2485a5455","added_by":"auto","created_at":"2026-04-21 10:57:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43099,"visible":true,"origin":"","legend":"\u003cp\u003eProjected changes in the spatial distribution of suitable habitats for \u003cem\u003eD. silvarum\u003c/em\u003e in the BTH Region. (A) 2021–2040; (B) 2041–2060; (C) 2061–2080; (D) 2081–2100.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/af94176d9c41cc8a1dd4fe68.jpeg"},{"id":107424406,"identity":"c76988a3-ebef-49f5-a93f-9af63635e7bb","added_by":"auto","created_at":"2026-04-21 10:57:33","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":56562,"visible":true,"origin":"","legend":"\u003cp\u003eProjected changes in the spatial distribution of suitable habitats for \u003cem\u003eI. persulcatus\u003c/em\u003e in the BTH Region. (A) 2021–2040; (B) 2041–2060; (C) 2061–2080; (D) 2081–2100.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/95d5d6dc4d543407ec7a66fb.jpeg"},{"id":107424388,"identity":"61c61c85-15e9-449b-8257-5e97a0e4f64b","added_by":"auto","created_at":"2026-04-21 10:57:30","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":80492,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the centroid of the potential suitability areas for the dominant tick species in BTH region. (A) 2021–2040; (B) 2041–2060; (C) 2061–2080; (D) 2081–2100.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/c748f8422b684bc844a7a9e1.jpeg"},{"id":107705662,"identity":"127937b0-4aec-4d1f-9b36-08a082521084","added_by":"auto","created_at":"2026-04-24 09:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":718713,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/29a06212-27d6-4f3c-9845-a9d4e1d151af.pdf"},{"id":107424385,"identity":"1836134e-f757-4180-8aca-7c8fe6153e5a","added_by":"auto","created_at":"2026-04-21 10:57:29","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10434,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/7aafd26b7a5dd8f52c16d15b.xlsx"},{"id":107424412,"identity":"7a03fc4a-1837-46b2-8fdb-54d271965461","added_by":"auto","created_at":"2026-04-21 10:57:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":437594,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/2a606497eb8e2ab86bf3ee16.pdf"},{"id":107424414,"identity":"3dbde1f7-b2ad-488f-b1ed-6742e7a4f9a7","added_by":"auto","created_at":"2026-04-21 10:57:34","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":987453,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/58ac9c69bdde43c9f6852e4c.pdf"},{"id":107489214,"identity":"102c8df3-cbed-4230-bfe2-f80e44bb31f7","added_by":"auto","created_at":"2026-04-22 02:46:54","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21759503,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251988/v1/41af00c93bfc03bafa0ed1b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diversity and spatiotemporal atlas of ticks in the Beijing-Tianjin-Hebei urban agglomeration based on MaxEnt model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTicks are obligate hematophagous ectoparasites that infest domestic animals, wildlife, and humans, and have been considered the second most important vectors of human diseases worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, 124 known tick species had been recorded, comprising 113 hard ticks (Ixodidae) and 11 soft ticks (Argasidae), which revealed substantial heterogeneity in tick diversity across the country [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to being major ectoparasites of animals, ticks served as vectors and reservoirs for numerous zoonotic pathogens, including bacteria, viruses, and protozoa, with increasing diversity over the past 30 years [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and posing serious threats to both human and animal health [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Climate change could indirectly affect the altering vegetation distribution, host abundance, human behavior, and land use, all of which influenced tick density [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, understanding the spatial distribution of dominant tick species under current and future climatic conditions is critical for effective prevention and control of tick-borne diseases.\u003c/p\u003e \u003cp\u003eThe various models had been developed to predict species distributions, including the Maximum Entropy (MaxEnt) model, Classification and Regression Trees (CART), Generalized Linear Models (GLMs), Habitat models (HABITAT), Genetic Algorithm for Rule-set Prediction (GARP), and Bioclimatic models (BIOCLIM) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among these, MaxEnt has become one of the most widely used due to its solid theoretical foundation in ecological niche modeling. Based on the principle of maximum entropy, the model estimated a species' potential habitat distribution from occurrence data and environmental variables, providing the least biased prediction that remained consistent with known information, even with limited data. The MaxEnt model was known for its high predictive accuracy, stability, and ease of interpretation, particularly when applied to species with sparse or discontinuous records [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], making it especially suitable for modeling the distribution of ticks and other vector species.\u003c/p\u003e \u003cp\u003eThe BTH region, located in northern China, was the country\u0026rsquo;s largest urban agglomeration in this area and features complex topography and ecosystems, including plains, hills, and mountains. These diverse habitats, along with rich animal resources, provided ideal breeding grounds and refuges for tick populations. Several tick-borne diseases, including spotted fever group rickettsiosis and severe fever with thrombocytopenia syndrome (SFTS), had been documented in this region [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent advances in microbiome research had further highlighted the complexity of pathogen dynamics. For instance, a global longitudinal analysis of \u003cem\u003eCampylobacter\u003c/em\u003e spp. antimicrobial resistance provided critical context for understanding zoonotic threats [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], while novel bioinformatic tools now enabled the detection of microbial biosynthetic gene clusters, offering new avenues for exploring pathogen-host interactions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In recent years, the BTH region has undergone rapid urbanization and population growth while also implementing strict environmental protection policies. This dual process has expanded the extent of human activity, increasing the likelihood of human exposure to ticks and tick-borne pathogens. Therefore, applying the MaxEnt model in conjunction with ArcGIS spatial analysis to investigate the distribution and future trends of dominant tick species in the BTH region is of great significance for public health surveillance and disease prevention.\u003c/p\u003e \u003cp\u003eIn this study, we provided a comprehensive overview of the spatial distribution of tick species in the BTH region of China. By integrating the MaxEnt model with ArcGIS spatial analysis, we predicted the potential suitable habitats and projected future distribution shifts of dominant tick species under both current and future climatic scenarios. These findings provide critical insights for regional tick-borne disease surveillance and risk assessment, establishing a scientific basis for the development of targeted prevention and control strategies.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Collection of tick geographical distribution data\u003c/h2\u003e \u003cp\u003eThe BTH urban agglomeration is located in the northern part of the North China Plain and encompasses 13 cities, including Beijing, Tianjin, and 11 cities in Hebei Province. Data on tick distribution were collected through literature retrieval and data extraction. We searched major scientific citation databases, including PubMed (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Web of Science, China National Knowledge Infrastructure (CNKI), and the Wanfang Database, using the keywords: \u0026ldquo;tick,\u0026rdquo; \u0026ldquo;Beijing,\u0026rdquo; \u0026ldquo;Hebei,\u0026rdquo; and \u0026ldquo;Tianjin.\u0026rdquo; The search covered all relevant publications available up to July 25th, 2024. In addition, we retrieved occurrence records 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) and the authoritative monograph Fauna Sinica: Arachnida: Ixodida from the Fauna of China series. Relevant distributional data and collection locations were extracted using the same procedures. We manually reviewed the titles, abstracts, and full texts of the articles and selected those that reported tick distribution in the BTH region. From these, we extracted geographic location and coordinate information. If longitude and latitude were not explicitly provided, we used the Baidu Map Coordinate Picker (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://api.map.baidu.com/lbsapi/getpoint/index.html\u003c/span\u003e\u003cspan address=\"https://api.map.baidu.com/lbsapi/getpoint/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to determine approximate coordinates based on the described locations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection of environmental variables\u003c/h2\u003e \u003cp\u003eEnvironmental variables used in this study included both bioclimatic and geographic factors. Nineteen bioclimatic variables (Bio1\u0026ndash;Bio19) were obtained from the WorldClim database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at a spatial resolution of 2.5 arc-minutes. To avoid model overfitting due to spatial autocorrelation among occurrence points, we used the SDMToolbox v2.5 plugin in ArcGIS to spatially rarefy the presence records.\u003c/p\u003e \u003cp\u003ePearson correlation analysis was then conducted in R to assess collinearity among variables. Variables with an absolute correlation coefficient |r| \u0026gt; 0.8 were considered collinear. Among these, variables with higher ecological contribution were retained. The geographic data, including elevation, slope, and aspect, were derived from the ASTER Global Digital Elevation Model (GDEM) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdemdl.aster.jspacesystems.or.jp/index_en.html\u003c/span\u003e\u003cspan address=\"https://gdemdl.aster.jspacesystems.or.jp/index_en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Slope and aspect were calculated using ArcGIS 10.4. Normalized Difference Vegetation Index (NDVI) data were obtained from the MOD13A3 dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.earthdata.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://www.earthdata.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Vector data for provincial administrative boundaries in the BTH region were acquired from the National Geomatics Center of China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tianditu.gov.cn/\u003c/span\u003e\u003cspan address=\"https://www.tianditu.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under the cartographic license number GS (2024) 0650, with the coordinate system set to GCS_WGS_1984.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction of the MaxEnt model\u003c/h2\u003e \u003cp\u003eFor the dominant tick species identified, the MaxEnt (Maximum Entropy) model was applied to analyze the key environmental factors influencing their distribution in the BTH region. A total of 75% of the occurrence records were randomly selected as the training dataset, while the remaining 25% were used for testing and validation of the model performance. The convergence threshold was set to 10\u003csup\u003e\u0026minus;\u003c/sup\u003e⁴, and the maximum number of iterations was set to 500. For each dominant species, ten bootstrap replicates were performed to ensure robustness of the predictions.\u003c/p\u003e \u003cp\u003eThe refined species occurrence data and environmental variables were treated as the input data by the MaxEnt software. The jackknife method was employed to evaluate the relative contribution of each environmental variable. Univariate response curves were constructed to determine the environmental suitability range for each species.\u003c/p\u003e \u003cp\u003eThe accuracy of the model prediction was assessed using the Receiver Operating Characteristic (ROC) curve, with the area under the curve (AUC) as the evaluation metric. AUC values range from 0 to 1, where values below 0.6 indicate poor predictive performance, 0.6\u0026ndash;0.7 suggest low accuracy, 0.7\u0026ndash;0.8 indicate moderate accuracy, 0.8\u0026ndash;0.9 reflect good predictive ability, and values above 0.9 denote excellent performance. The closer the AUC value is to 1, the more accurate the prediction and the stronger the correlation between species distribution and environmental variables.\u003c/p\u003e \u003cp\u003eWith the ArcGIS 10.4, the mean results of the MaxEnt replicates were used to generate a habitat suitability index (HSI) ranging from 0 to 1. Higher values indicate more suitable habitats. The HSI values were classified into four suitability levels: high suitability, moderate suitability, low suitability, and unsuitable. MaxEnt modeling was conducted by using the MaxEnt software, geospatial analysis was performed in ArcGIS 10.8, and plotting was conducted in R v4.3.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Projection of future habitat suitability\u003c/h2\u003e \u003cp\u003eFuture climate data were obtained from the BCC-CSM2.MR model, a high-resolution climate projection model developed under the Coupled Model Intercomparison Project Phase 6 (CMIP6). This model includes atmosphere, land surface, ocean and sea ice.\u003c/p\u003e \u003cp\u003eThe future scenario selected in this study was SSP245, an upgraded version of the medium-intensity SSP2 pathway, comparable to the RCP4.5 scenario. According to climate projections, RCP4.5 peaks around 2040 and stabilizes before 2080, which aligns with China's future development trajectory. Therefore, four future time periods were selected under the SSP245 scenario: 2021\u0026ndash;2040, 2041\u0026ndash;2060, 2061\u0026ndash;2080, and 2081\u0026ndash;2100. Environmental variables for each future period were combined with the current topographic and species occurrence data and re-entered into the MaxEnt model to simulate the potential distribution of dominant tick species in the BTH region under future climate scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Centroid shift of suitable habitats\u003c/h2\u003e \u003cp\u003eTo evaluate spatial changes in suitable habitats over time, we analyzed the shifts in the geographic centroids of habitat suitability for each dominant tick species from the present day to the time period of 2081\u0026ndash;2100. Binary transformation of the habitat suitability maps generated by MaxEnt was conducted using ArcGIS 10.4 to extract the coordinates of the most suitable regions. By linking the centroid coordinates across time periods, we identified both the direction and magnitude of habitat centroid shifts, revealing temporal trends in the geographic distribution of suitable habitats under climate change.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution of tick species in the Beijing-Tianjin-Hebei region\u003c/h2\u003e \u003cp\u003eA total of 1446 articles were retrieved, including 784 Chinese literature (462 from CNKI, 322 from Wanfang) and 662 foreign literatures (633 from PubMed, 29 from Web of Science). After removing duplicates and articles with unclear or irrelevant distribution information (data cleaning), 40 articles were screened for the final analysis, with 33 Chinese literature and 7 foreign literatures (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, based on both our own field sampling records and occurrence data extracted from published literature, we compiled the distribution data of tick species in the BTH region. In total, 19 tick species belonging to 6 genera have been recorded to date across the BTH region, with distribution mapped at the prefecture level or below (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eHae. longicornis\u003c/em\u003e was found to be widely distributed in both plain and mountainous areas of the region, while other tick species were primarily recorded in the northern part of the BTH region. Based on the known distribution patterns of various tick species across different ecological habitats and the availability of occurrence data, we selected four dominant tick species in the region for subsequent ecological niche modeling analyses. A total of 167 occurrence records were compiled from 55 publications, with \u003cem\u003eHae. longicornis\u003c/em\u003e accounting for the majority (116 records from 30 publications), followed by \u003cem\u003eD. silvarum\u003c/em\u003e (31 records from 12 publications), \u003cem\u003eI. persulcatus\u003c/em\u003e (11 records from 10 publications), and \u003cem\u003eHae. concinna\u003c/em\u003e (9 records from 3 publications).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Environmental variables influence tick distribution\u003c/h2\u003e \u003cp\u003eTo construct species-specific MaxEnt models for different tick species, an initial set of 19 bioclimatic variables was considered. The species-specific subsets of variables were then selected based on correlation analysis. The contributions of environmental variables were assessed by using the Jackknife test, with the key influencing variables for each species summarized (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, to investigate the distribution characteristics of ticks, the environmental factors affecting the current and potential distribution of tick species had been analyzed (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-D). The distribution of \u003cem\u003eHae. longicornis\u003c/em\u003e and \u003cem\u003eHae. concinna\u003c/em\u003e was mainly influenced by elevation, mean temperature of the coldest quarter (Bio11), slope, and NDVI. For \u003cem\u003eD. silvarum\u003c/em\u003e, the key variables were elevation, slope, and NDVI. The distribution of \u003cem\u003eI. persulcatus\u003c/em\u003e was primarily driven by Bio11 and aspect. The probability of occurrence for \u003cem\u003eHae. longicornis\u003c/em\u003e and \u003cem\u003eD. silvarum\u003c/em\u003e reaches its maximum at elevations of 184.36 meters and 1180.58 meters, respectively, and at temperatures of -4.50\u0026deg;C and \u0026minus;\u0026thinsp;18.26\u0026deg;C. Furthermore, the regularized training gain results of the MaxEnt models, as shown in the Jackknife plots (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-D), revealed that when using single environmental variables, elevation contributed the highest gain for \u003cem\u003eHae. longicornis\u003c/em\u003e, followed by Bio11, slope, Bio3, and Bio16. For \u003cem\u003eHae. concinna\u003c/em\u003e, Bio11 exhibited the highest gain, followed by NDVI, slope, and Bio17. For \u003cem\u003eD. silvarum\u003c/em\u003e, elevation had the highest gain, followed by Bio11, slope, Bio3, and Bio16. For \u003cem\u003eI. persulcatus\u003c/em\u003e, Bio11 showed the highest gain, followed by aspect and Bio3. Based on the results of the pearson correlation analysis, the final selected variables for modeling were as follows: for \u003cem\u003eHae. longicornis\u003c/em\u003e, ten variables were selected, including Bio2, Bio3, Bio11, Bio14, Bio15, Bio16, elevation, slope, aspect, and NDVI; for \u003cem\u003eHae. concinna\u003c/em\u003e, six variables were selected, including Bio3, Bio11, Bio17, slope, aspect, and NDVI; for \u003cem\u003eD. silvarum\u003c/em\u003e, nine variables were selected, including Bio2, Bio3, Bio7, Bio12, Bio17, elevation, slope, aspect, and NDVI; and for \u003cem\u003eIxodes persulcatus\u003c/em\u003e, five variables were selected, including Bio3, Bio11, slope, aspect, and NDVI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Suitable habitat for dominant tick species under current climate conditions\u003c/h2\u003e \u003cp\u003eBased on ten replicates of MaxEnt runs with 19 environmental variables, the average AUC values for \u003cem\u003eHae. longicornis\u003c/em\u003e, \u003cem\u003eHae. concinna\u003c/em\u003e, \u003cem\u003eD. silvarum\u003c/em\u003e, and \u003cem\u003eI. persulcatus\u003c/em\u003e were 0.88, 0.88, 0.91, and 0.86, respectively, indicating high predictive performance (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE-H). According to the AUC curve evaluation criteria, a higher AUC value for the test set indicates better model performance. In this study, the high AUC values obtained demonstrate that the predictive models performed well. Predicted suitable habitats for these species in the BTH region were classified into four categories: highly suitable, moderately suitable, low suitability, and unsuitable areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Under current climatic conditions, the distributions of the four tick species exhibit both spatial overlap and distinct patterns. \u003cem\u003eHae. longicornis\u003c/em\u003e shows a characteristic belt-shaped distribution, with areas of moderate to high suitability extending along the Taihang and Yanshan mountain ranges in a north-south orientation. In contrast, most of the southern plain areas of the BTH region are predicted to be of low or unsuitable habitat suitability for this species. For \u003cem\u003eHae. concinna\u003c/em\u003e, the high-suitability zones are mainly concentrated in the northern mountainous areas of Hebei province, including the Chengde, northern Tangshan, and Zhangjiakou regions, suggesting a strong ecological adaptation to local environmental conditions. The suitable habitat for \u003cem\u003eDermacentor silvarum\u003c/em\u003e was also predominantly located in the northern mountainous areas of the BTH region, particularly in the Zhangjiakou, Chengde, and northern Baoding regions of Hebei province, where high habitat suitability had been predicted. In contrast, the southern and southwestern parts of BTH were largely unsuitable or show low suitability for this species. \u003cem\u003eIxodes persulcatus\u003c/em\u003e was predicted to have suitable habitats mainly in the northern peripheral areas of BTH, particularly near the border between Hebei province and the BTH region, such as the northern part of Zhangjiakou and Chengde. Habitat suitability decreases substantially in the central and southern plain areas. Overall, the distribution patterns of these four tick species were clearly shaped by topography and climate, with suitable habitats primarily located in higher-elevation mountainous and hilly regions characterized by more moderate climatic conditions. The results here provided a valuable spatial framework to inform targeted surveillance and control of tick-borne diseases in the BTH region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Prediction of spatial distributions for tick species under the coming decades\u003c/h2\u003e \u003cp\u003eThe potential suitability areas for dominant tick species were predicted in the coming periods. During 2081\u0026ndash;2100, the potentially suitable habitat for \u003cem\u003eHae. longicornis\u003c/em\u003e was projected to increase by 35,583 km\u0026sup2;. Areas of moderate and low suitability were each projected to expand by 8,266 km\u0026sup2;, primarily around Zhangjiakou, Chengde, and the peripheral areas of Beijing. In contrast, approximately 106,186 km\u0026sup2;, mostly in the southeastern plains, would become unsuitable. This species was expected to expand its range steadily throughout the 21st century. The persistent risk in western highlands and low-altitude valleys underscored the need for long-term monitoring and region-specific vector control strategies under climate change scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For \u003cem\u003eHae. concinna\u003c/em\u003e, the high-suitability zones would gradually shrink and became more fragmented, particularly in the northern areas. Although suitable regions persisted, their density decreased, likely due to habitat fragmentation or environmental constraints caused by climatic fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The \u003cem\u003eD. silvarum\u003c/em\u003e was projected to remain confined to the ecologically favorable northwestern regions, particularly the Zhangjiakou area of Hebei province. Its high-suitability area is expected to increase by 433 km\u0026sup2; by 2081\u0026ndash;2100, though inter-period fluctuations suggest climate variability could impact its spatial stability. Targeted surveillance in these high-risk areas is warranted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The \u003cem\u003eI. persulcatus\u003c/em\u003e is expected to remain largely restricted to northern and northwestern areas with cooler climates and dense vegetation. While central and southern plains remain unsuitable, the total suitable area may increase by 16,673 km\u0026sup2; during the time period of 2081\u0026ndash;2100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Only the moderately suitable area is projected to expand, while highly and low-suitability zones are expected to decline. These findings highlight the necessity of prioritizing monitoring efforts in the northern high-risk zones to enable early warning and control of \u003cem\u003eI. persulcatus\u003c/em\u003e and its associated pathogens under changing climate conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Changes in centroid of the potential suitability areas in different future periods\u003c/h2\u003e \u003cp\u003eTo assess the future spatial dynamics of dominant tick species, the changes in their potential habitat centroided across different time periods were further analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). By 2081\u0026ndash;2100, \u003cem\u003eHae. longicornis\u003c/em\u003e was projected to shift its potential suitability centroid approximately 57.6 km northwest, with a latitude increase of about 0.5\u0026deg; and a longitude change of 0.14\u0026deg;, indicating a trend of northward expansion. Similarly, \u003cem\u003eHae. concinna\u003c/em\u003e was expected to shift about 63.0 km to the northeast, with changes of approximately 0.4\u0026deg; in latitude and 0.5\u0026deg; in longitude, reflecting a northeastward expansion. The \u003cem\u003eD. silvarum\u003c/em\u003e showed a projected centroid shift of around 71.1 km to the northwest, with a latitude change of 0.6\u0026deg; and a longitude change of 0.4\u0026deg;, suggesting a westward expansion of its suitable habitat. In contrast, \u003cem\u003eI. persulcatus\u003c/em\u003e is expected to shift its centroid approximately 50.0 km northward, with a latitude change of 0.4\u0026deg; and only a minor change in longitude. However, its overall potential suitability area still demonstrated a westward expansion trend. These results indicated species-specific but consistent directional shifts under future climatic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, based on tick distribution data from the BTH region, we provided a comprehensive summary of the distribution characteristics of tick species in the BTH area. By integrating climatic and environmental factors with ecological and geographical features, the MaxEnt model was employed to analyze the habitat suitability of dominant tick species and to predict their current and future potential spatial distributions. A total of 19 tick species from 6 genera were revealed in the BTH region, with \u003cem\u003eHae. longicornis\u003c/em\u003e, \u003cem\u003eHae. concinna\u003c/em\u003e, \u003cem\u003eD. silvarum\u003c/em\u003e, and \u003cem\u003eI. persulcatus\u003c/em\u003e being the dominant species. The suitable habitat ranges varied among species, with \u003cem\u003eHae. longicornis\u003c/em\u003e and \u003cem\u003eD. silvarum\u003c/em\u003e exhibiting expanding trends. Model performance evaluation indicated high predictive reliability for the 4 dominant species, with AUC values of 0.88, 0.88, 0.91 and 0.86, respectively.\u003c/p\u003e \u003cp\u003eThe distribution of different tick species varied across regions. \u003cem\u003eHae. longicornis\u003c/em\u003e exhibited a wide distribution, spanning forested areas, mountainous regions, and plains. The expanding global distribution of \u003cem\u003eHae. longicornis\u003c/em\u003e, coupled with its capacity to carry a wide array of pathogens, positioned this species as an emerging vector of significant public health concern. Originally native to eastern and central Asia, \u003cem\u003eHae. longicornis\u003c/em\u003e had successfully invaded regions such as Australia, New Zealand, the Pacific Islands, and the United States, largely owing to its capacity for parthenogenetic reproduction, whereby a single female tick can establish a population [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eHae. longicornis\u003c/em\u003e is also an efficient vector capable of transmitting multiple viruses, including Powassan virus, Khasan virus, tick-borne encephalitis virus, and severe fever with thrombocytopenia syndrome virus (SFTSV), all of which are increasing in incidence and geographic range, posing serious threats to both human health and animal welfare [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As the predominant tick species in the BTH region, \u003cem\u003eHae. longicornis\u003c/em\u003e harbored a diverse array of medically and veterinary important pathogens, including \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e, \u003cem\u003eAnaplasma ovis\u003c/em\u003e and \u003cem\u003eAnaplasma capra\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous studies showed that \u003cem\u003eRhipicephalus microplus\u003c/em\u003e in the BTH region harbored multiple pathogens, including \u003cem\u003eRickettsia raoultii\u003c/em\u003e, \u003cem\u003eBorrelia garinii\u003c/em\u003e, \u003cem\u003eBabesia venatorum\u003c/em\u003e, \u003cem\u003eA. ovis\u003c/em\u003e and \u003cem\u003eA. phagocytophilum\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In contrast, \u003cem\u003eHae. concinna\u003c/em\u003e, \u003cem\u003eD. silvarum\u003c/em\u003e, and \u003cem\u003eI. persulcatus\u003c/em\u003e were primarily found in the grassland\u0026ndash;forest ecotones of northern BTH area, such as Zhangjiakou and Chengde. The tick \u003cem\u003eHae. concinna\u003c/em\u003e was known to carry a range of pathogens, including \u003cem\u003eR. raoultii\u003c/em\u003e and \u003cem\u003eBabesia venatorum\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. \u003cem\u003eD. silvarum\u003c/em\u003e was capable of carrying a wide range of tick-borne pathogens, including tick-borne encephalitis virus, \u003cem\u003eR. slovaca\u003c/em\u003e, \u003cem\u003eR. raoultii\u003c/em\u003e, \u003cem\u003eA. phagocytophilum\u003c/em\u003e, \u003cem\u003eBabesia caballi\u003c/em\u003e, \u003cem\u003eTheileria equi\u003c/em\u003e, as well as \u003cem\u003eEhrlichia chaffeensis\u003c/em\u003e, the causative agent of human monocytic ehrlichiosis [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, with the advancement of high-throughput sequencing technologies, several novel tick-borne viruses had been identified in \u003cem\u003eD. silvarum\u003c/em\u003e in recent years, such as the Tacheng tick virus 1 (TcTV-1) and Jingmen tick virus (JMTV) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The \u003cem\u003eI. persulcatus\u003c/em\u003e was one of the most important vectors of zoonotic diseases in the Northern Hemisphere, with a wide distribution across the Eurasian continent [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It readily bit humans and was confirmed to transmit multiple microbial pathogens, including tick-borne encephalitis virus (TBEV), \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e, \u003cem\u003eBabesia divergens\u003c/em\u003e, and \u003cem\u003eBorrelia afzelii\u003c/em\u003e, all of which caused multi-organ and multi-system damage [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In addition, the newly identified tick-borne virus, \u003cem\u003eAlongshan\u003c/em\u003e virus, discovered in 2019, had also been closely associated.\u003c/p\u003e \u003cp\u003eBetween 2009 and 2012, clinical cases in Hebei Province suspected of rickettsial infections were screened for emerging \u003cem\u003eA. phagocytophilum\u003c/em\u003e and spotted fever group \u003cem\u003eRickettsiae\u003c/em\u003e (SFGR). The results revealed that 10.9% of the cases tested positive for emerging SFGR, while 8.9% were positive for emerging \u003cem\u003eA. phagocytophilum\u003c/em\u003e, indicating the presence and public health relevance of these emerging tick-borne pathogens in the region [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These ecologically diverse and logistically active areas supported the development and spread of ticks and their pathogens. The current and future suitable habitats of the four dominant tick species were predicted under various ecological conditions using the MaxEnt model. The findings offered insights into the ecological characteristics and future risk landscapes of tick populations in the BTH region, providing a scientific basis for disease surveillance and early-warning systems.\u003c/p\u003e \u003cp\u003eAnalysis of crucial climatic and environmental variables revealed that Bio11, NDVI, and topography significantly influenced the distribution of \u003cem\u003eHae. longicornis\u003c/em\u003e, \u003cem\u003eHae. concinna\u003c/em\u003e, \u003cem\u003eD. silvarum\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e. Notably, their potential suitable habitats showed considerable overlap. The predicted distribution probabilities of \u003cem\u003eHae. longicornis\u003c/em\u003e and \u003cem\u003eHae. concinna\u003c/em\u003e peaked at elevations of 184.36 m and 1180.58 m, respectively, indicating inter-specific differences in altitude preferences among \u003cem\u003eHaemaphysalis\u003c/em\u003e ticks. That suggested that the \u003cem\u003eHae. longicornis\u003c/em\u003e tended to inhabit lower elevations and had a broader ecological niche. Furthermore, the probability of occurrence for all four species peaked at temperatures of -4.50\u0026deg;C and \u0026minus;\u0026thinsp;18.26\u0026deg;C, demonstrating the strong temperature sensitivity of ticks [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Under current climatic conditions, the suitable habitat of \u003cem\u003eHae. longicornis\u003c/em\u003e encompassed approximately 33.85% of the total area of the BTH region, indicating a widespread distribution.\u003c/p\u003e \u003cp\u003eFrom a broader temporal perspective, notable shifts in the potential suitable habitats of these four tick species were projected under future climatic scenarios (2081\u0026ndash;2100). There were several studies that were consistent with our model. For instance, previous researches revealed that the northeastern forest region would become warmer and suitable for \u003cem\u003eDermacentor nuttalli\u003c/em\u003e due to global warming and land-use changes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Besides, the studies predicted an expansion of suitable habitats for \u003cem\u003eI. persulcatus\u003c/em\u003e in BTH by 2070 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thus, there was evident that the combined effects of climate change, human activities, land use, and vector population growth would lead to the expansion of suitable habitat areas for the dominant tick species in the BTH region. In line with this, this study showed that by 2081\u0026ndash;2100, the habitat centers of \u003cem\u003eHae. longicornis\u003c/em\u003e, \u003cem\u003eI. persulcatus\u003c/em\u003e, and \u003cem\u003eD. silvarum\u003c/em\u003e would be expected to shift northwestward, with localized expansions in the Zhangjiakou and Chengde regions of Hebei province. In contrast, \u003cem\u003eHae. concinna\u003c/em\u003e was projected to exhibit a northeastward habitat shift, with new suitable areas emerging in the Chengde area of Hebei province. These distributional changes may be attributable to ongoing ecological restoration projects in the BTH region, including cropland-to-forest conversion, afforestation subsidies and forestry compensation schemes, which had significantly enhanced vegetation cover and mitigated desertification [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the distribution patterns of tick species in the BTH region by using comprehensive literature and field-collected data. By integrating species occurrence records with key bioclimatic variables, we applied the MaxEnt model and spatial analysis tools to predict the current and future distributions of the dominant tick species under climate change scenarios. These findings underscored the dynamic nature of tick habitat suitability under changing environmental conditions and highlighted the potential impact of ecological engineering on the bio-geographic patterns of medically important tick species. This study represented the first integrated effort to systematically map tick species distributions in BTH region by using a combination of literature and field-collected data, highlighting current and projected distribution shifts of four medically important tick species. The identification of future high-risk areas would help guide disease surveillance and control strategies, optimize resource allocation, and reduce the risk of tick-borne disease outbreaks. Nonetheless, there were still some limitations in this study. First, tick distribution data were primarily derived from published literature, which may be incomplete or biased, potentially underestimating the true distribution range. To address sampling bias, we removed duplicate records from the same locations and applied minimum distance thresholds for sampling points. Second, the MaxEnt model predicted the potential habitat suitability, not the actual distribution or population density. This model only accounted for abiotic factors and did not incorporate biotic interactions such as host availability, which may explain the presence of ticks in areas predicted to be unsuitable. Host movement patterns, population dynamics, and human activity were not considered. Finally, the absence of long-term field validation data limits our ability to assess the accuracy of projected distribution shifts over time. Despite these limitations, this study provides the first systematic spatial assessment of dominant tick species in the BTH region and offers valuable baseline information for future surveillance and control strategies.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, in this study, we provided a systematic overview of tick diversity and distribution patterns in the BTH region based on consolidated regional data. By integrating the MaxEnt model with ArcGIS spatial analysis, we projected the potential suitable habitats of four dominant tick species under current and future climate scenarios. These findings offered a scientific foundation for advancing tick research and strengthening regional surveillance and control strategies for tick-borne diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp skip=\"true\"\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eNot applicable\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003eCompeting of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Youth Fund of the Natural Science Foundation of Tianjin (24JCQNJC00460).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation and methodology, S.L.J.; Software, W.Y.G.; Validation, L.L.C. and Z.L.W.; Formal analysis, L.L.C. and Y.S.; Investigation, L.L.C. and Z.L.W.; Data curation, J.Q.N., H.N.C. and X.J.; Writing—original draft preparation, L.L.C.; Writing—review and editing, W.Y.G., S.L.J. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe computational requirements for this work were supported by the High-performance Computing Platform of Tianjin Medical University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJongejan F, Uilenberg G. The global importance of ticks. Parasitology. 2004; 129 Suppl: S3-14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao GP, Wang YX, Fan ZW, et al. Mapping ticks and tick-borne pathogens in China. Nat Commun. 2021;12:1075.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang LQ, Liu K, Li XL, et al. Emerging tick-borne infections in mainland China: an increasing public health threat. Lancet Infect Dis. 2015;15(12):1467\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKilpatrick AM, Randolph SE. 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Projecting the potential distribution of ticks in China under climate and land use change. Int J Parasitol. 2021;51(9):749\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa R, Li C, Tian H, et al. The current distribution of tick species in Inner Mongolia and inferring potential suitability areas for dominant tick species based on the MaxEnt model. Parasit Vectors. 2023;16(1):286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang J, Zhong M, Zeng G, Chen G, Hua S, Li X, Yuan Y, Wu H, Gao X. Risk management for optimal land use planning integrating ecosystem services values: A case study in Changsha, Middle China. Sci Total Environ. 2017;579:1675\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ticks and tick-borne diseases, MaxEnt model, Beijing-Tianjin-Hebei (BTH) region, Suitability area, Environmental factors","lastPublishedDoi":"10.21203/rs.3.rs-9251988/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9251988/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis integrative assessment delineates present and projected hotspots for four dominant tick species in the Beijing-Tianjin-Hebei (BTH) region, providing a spatial basis for targeted surveillance and control of tick-borne diseases.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive review of the latest literature to determine the current distribution of ticks in the BTH region. Subsequently, the MaxEnt model was used to analyze the climate and environmental factors that affect the distribution of dominant ticks, and simulated the spatio-temporal distribution characteristics of ticks in the BTH region in the future.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTotally 19 tick species from 6 genera in the BTH region, including four dominant tick species such as \u003cem\u003eHaemaphysalis longicornis\u003c/em\u003e, \u003cem\u003eHaemaphysalis concinna\u003c/em\u003e, \u003cem\u003eDermacentor silvarum\u003c/em\u003e and \u003cem\u003eIxodes persulcatus\u003c/em\u003e. \u003cem\u003eHae. longicornis\u003c/em\u003e exhibited a widespread distribution, while \u003cem\u003eHae. concinna\u003c/em\u003e, \u003cem\u003eD. silvarum\u003c/em\u003e, and \u003cem\u003eI. persulcatus\u003c/em\u003e were predominantly found in the northern and northwestern parts of BTH region. The main environmental variables affecting their distributions were temperature (Bio11), elevation and normalized difference vegetation index (NDVI). The model predictions indicated that the suitable habitats of all four dominant species would experience varying degrees of fluctuation under future climate conditions. Specifically, during 2081\u0026ndash;2100, the centroid of suitable habitats for \u003cem\u003eHae. longicornis\u003c/em\u003e, \u003cem\u003eD. silvarum\u003c/em\u003e, and \u003cem\u003eI. persulcatus\u003c/em\u003e is predicted to shift northwestward, while \u003cem\u003eHae. concinna\u003c/em\u003e is expected to shift northeastward.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this study, we provided a comprehensive assessment of tick species composition and spatial distribution patterns in the BTH region, which could provide a valuable reference for future research on tick distribution and the surveillance of tick-borne diseases in the region.\u003c/p\u003e","manuscriptTitle":"Diversity and spatiotemporal atlas of ticks in the Beijing-Tianjin-Hebei urban agglomeration based on MaxEnt model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 10:56:43","doi":"10.21203/rs.3.rs-9251988/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87a95019-1ec1-4ae1-828d-9f9fd9f4bc69","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T21:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 10:56:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9251988","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9251988","identity":"rs-9251988","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}