Spatiotemporal dynamics of Rattus tanezumi density and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China

Spatiotemporal dynamics of Rattus tanezumi density and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China | 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 Spatiotemporal dynamics of Rattus tanezumi density and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China Hansen Xiao, Qiyong Liu, Kun Su, Zheng Wang, Taotian Tu, Jing Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7896124/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Rattus tanezumi ( R. tanezumi ), a major vector for plague, leptospirosis, and hantavirus, exhibits population dynamics in the Three Gorges Reservoir (TGR) Area of China that are influenced by climate and reservoir-induced environmental changes, thereby impacting disease transmission risks. This study investigated the spatiotemporal dynamics of R. tanezumi and its association with key meteorological and environmental factors from 2015 to 2021. Using Geographic Information Systems (GIS) for spatiotemporal analysis and a Generalized Additive Model (GAM), we identified the current month's average temperature and relative humidity, together with three-month-lagged precipitation and the Normalized Difference Vegetation Index (NDVI), as significant drivers. The relationship was modeled (Yi,t = α + s 1 (M0Avg_Temp) + s 2 (M0Avg_RHU) + s 3 (M3Precip) + s 4 (M3NDVI) + ε; R²= 0.12, Deviance explained = 14%, GCV = 0.4438), revealing an optimal proliferation temperature around 20°C. Density showed negative correlations with both current relative humidity and lagged precipitation, and a fluctuating decline with increasing lagged NDVI. As the first investigation of its kind, this study demonstrates how climatic and environmental factors shape the spatiotemporal distribution of R. tanezumi in the TGR Area, providing critical insights for predicting outbreaks and formulating targeted surveillance and control strategies against rodent-borne diseases in this vulnerable region. Rattus tanezumi Three Gorges Reservoir spatiotemporal dynamics environmental drivers GAM Disease ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The R.tanezumi , also called Asian house rat (1), is a highly adaptable murid rodent that has established a commensal relationship with human populations across tropical and subtropical Asia(2, 3). R.tanezumi serves as a host and vector for numerous pathogens(4). It can spread a wide range of vector-borne diseases and zoonoses, including plague, hemorrhagic fever with renal syndrome, leptospirosis, through direct contact, the carriage of parasites, and fecal contamination(5-7). In addition, it’s also the main pest in farmland in the word, that causes damage to crops in spring and autumn before harvest(8, 9). Due to its wide geographical distribution, high density and frequent interaction with humans, it poses a major threat to human health(10, 11). The Three Gorges project is one of the largest hydropower projects in the world. It is in the Yangtze River Basin of China and involves more than 20 districts and counties in Chongqing. The TGR area has a unique ecological background, mild and humid climate, rich water resources and complex ecosystem, which provide suitable conditions for the breeding and disease transmission of vectors such as R.tanezumi (12). It is reported that in the investigation of rodents in the Chongqing section of the TGR area, pathogenic agents such as Hantavirus, pathogenic Leptospira, and Bartonella were found to be carried(13). In addition, the construction of the Three Gorges Project, large-scale human activities, and climate change have led to significant alterations in the ecological environment of the reservoir area(14). After the completion of the TGR, the water level for winter storage and power generation is maintained at 175 m, while during the summer flood season, the water level is lowered to 145 m for flood control. With the periodic fluctuations in the reservoir water level, when water storage commences and the water level rises, rodents may migrate upward in response to the rising water level, potentially causing a local increase in rodent density and thereby posing a risk of rodent-borne diseases. This may alter the distribution, behavior, and population dynamics of R.tanezumi , subsequently changing its disease transmission potential and having cascading effects on the ecosystem and public health. Therefore, it is imperative to emphasize the development of surveillance, early warning, and prevention strategies for vector-borne diseases associated with R.tanezumi . Many studies have shown that the population composition, density, seasonal fluctuation and other ecological characteristics of vectors are closely related to the epidemic intensity of related diseases in the population(15-17). At the same time, these ecological characteristics of rodents, including R.tanezumi , are profoundly affected by meteorological factors such as temperature, precipitation, humidity and extreme weather events, and have also been recognized by the academic community(18-20). However, the specific drivers and their lagged effects on R. tanezumi populations within the unique context of the TGR's water-level fluctuation zone remain poorly quantified. In this study, we used the surveillance data of R.tanezumi density in eight districts and counties along the TGR Area in Chongqing from 2015 to 2021, to analyze the spatial-temporal changes and spatial correlation of R.tanezumi density through GIS system. Then, we explore the association between the R.tanezumi density and six explanatory variables (mainly involving climate and land cover) by using a generalized additive model (GAM)(21). This model can provide a reference for the study of the spatiotemporal dynamics of R.tanezumi under future climate and land cover changes, thereby contributing to the prevention and control of local rodent-borne diseases(22). Methods Setting of surveillance site From 2015 to 2021, the surveillance was carried out in eight districts or counties along the Chongqing section of the TGR area, namely Fuling, Wanzhou, Kaizhou, Fengdu, Zhong County, Yunyang, Fengjie, and Wushan. Each district or county selected two resettlement villages or towns less than 1 km away from the shoreline of the Yangtze river or its tributaries as surveillance site(Fig 1). The density of R.tanezumi was surveillanced in January, March, May, July, September and November each year. Methods for Assessing R.tanezumi density The cage trapping method with marinated pork (skin area of 1 cm×1 cm) as bait was used for the surveillance. Each survey lasted for 3 days, 100 mousetrap cages were placed in the indoor and cultivated land respectively very day. Indoor, mousetrap cage is placed every 15 square meters on average. In the cultivated land, mousetrap cage is placed every 5 meters along the line, and the row spacing of the cages were 50 meters. In addition, the cage is placed in the late and collected in the morning. The monthly density of R.tanezumi was calculated by dividing the number of captured R.tanezumi by the number of effective cages. Data sources of ecological environment Historical climate data and land cover data were collated on a monthly basis, including monthly average temperature(Avg_Temp), monthly average maximum temperature(Max_Temp), monthly average minimum temperature(Min_Temp), monthly cumulative precipitation(Precip), monthly average relative humidity(Avg_RHU), and monthly Normalized Difference Vegetation Index(NDVI), which were obtained from the website of Resource and Environmental Science Data Center, China(https://www.resdc.cn ). In addition, the above data from differing grid geometries were aggregated to district or county level by using ArcGIS 10.7 software. Modeling the density dynamics of R.tanezumi The GAM that has been successfully used to simulate the population density dynamics of some vectors was performed to explore the associations between monthly the density of R.tanezumi and above explanatory variables. Considering the possible lag effect of explanatory variables on response variables, this study conducted a lag of 0-3 months for explanatory variables. Overall, in the R language programming environment, the "GAM" function in the "mgcv" package of r4.2.2 was used, with the density of R.tanezumi as the response variable and meteorological and NDVI data with a lag of 0-3 months as the explanatory variables to establish the GAM model. Firstly, significant explanatory variables were screened through single-factor analysis (test level α =0.05), and generalized cross-validation (GCV), coefficient of determination (R 2 ), and explanatory bias (DE) were used as the model evaluation criteria, the smoothing parameter (k) for each term was automatically selected by the mgcv package using the REML method. Variables with statistical significance, lower GCV, higher R 2 and higher DE were included in GAM for multivariate analysis. Meanwhile, collinearity diagnosis was conducted for the meaningful variables in the multivariate analysis and they were included in the interaction model for analysis. Collinearity diagnosis was conducted by calculating the variance inflation factor (VIF) of each explanatory variable. The GAM model can be written as follows. Y i,t =α+s 1 (M i,l X 1 )+s 2 (M i,l X 2 ) +s 3 (M i,l X 3 )+…+s n (M i,l X n )+ε where i is the region, t the time (month), and s( ) means a smoothing function. Dependent variable Y i, t refers to the density of R.tanezumi in region i at time t (month). Variable l refers to the lagged month(0,1,2,3), The independent variable M i,l X n refers to the explanatory factors(Avg_Temp, Max_Temp, Min_Temp, Precip, Avg_RHU, NDVI) in region i and lag l month. The tensor product smoothing function te () was used to fit the influence of the interaction of the main meteorological factors on the mouse density, and the results were presented as a smooth surface. The basic model is as follows. Y i,t =α+te(M i,l X 1 , M i,l X 2 ) +s 3 (M i,l X 3 )+…+s n (M i,l X n )+ε Results Spatiotemporal distribution of R.tanezumi The Brown-Forsythe test confirmed the homogeneity of density values in various regions, thereby verifying the use of parametric analysis of variance. Among them, regional differences are significant( F =3.772, P =0.003). However, the Mann-Kendall trend test showed no significant temporal trend in density variation (τ = 0.143, P = 0.764)(Table 1). Spatial heterogeneity analysis using Moran's I index revealed predominantly random distribution patterns of R.tanezumi density across eight counties. However, significant negative spatial autocorrelation emerged in 2018 (Moran's I=-0.90, P =0.03) and 2020 (Moran's I = -0.83, P = 0.03), indicating a dispersed distribution where high-density clusters were adjacent to low-density areas (Fig 2). This checkerboard pattern suggests localized population regulation mechanisms during these years. It should be noted that due to COVID-19 pandemic restrictions, longitudinal monitoring of R.tanezumi populations in Wushan and Zhong Counties was interrupted in 2020, resulting in missing annual density records. These data gaps were addressed using adjacent-year mean imputation (AYMI), whereby missing 2020 values were replaced with the arithmetic mean of corresponding 2019 and 2021 measurements for each county. Table 1 Density of R.tanezumi in the TGR area from 2015 to 2021 Year Density of R.tanezumi (%) Total Density(%) Wanzhou Fuling Kaizhou Fengdu Fengjie Wushan Zhongxian Yunyang 2015 1.01 0.14 1.49 0.40 1.38 1.07 1.49 0.75 0.97 2016 1.51 0.35 0.86 1.24 1.57 1.33 1.24 1.29 1.17 2017 1.40 0.96 0.92 1.16 1.75 0.64 1.39 1.33 1.19 2018 1.05 1.25 1.82 1.38 1.75 0.87 1.21 0.51 1.23 2019 1.60 1.13 1.23 1.20 1.56 0.86 1.61 1.01 1.28 2020 1.23 1.35 0.95 0.61 1.16 1.02 a 1.56 a 1.19 1.13 2021 0.97 0.55 1.51 0.58 1.07 1.19 1.50 1.21 1.07 a Missing data imputed using adjacent-year mean imputation method. The D'Agostino & Pearson test was conducted to assess the homogeneity of variances across months for mouse density data, and the results confirmed the assumption of equal variances, thereby validating the appropriateness of employing one-way ANOVA for analyzing R.tanezumi density in different months. The one-way ANOVA results revealed significant monthly variations in mouse density ( F = 3.001, P = 0.021). Specifically, the density of R.tanezumi was relatively low in January, and it gradually increased in March and May as spring approached. By autumn (November), the density began to decline. Additionally, an independent samples t-test was performed to compare R.tanezumi density between the two groups of the impoundment period (January, March, November) and the discharge period (May, July, September) of the TGR. The results showed a statistically significant difference in R.tanezumi density between these two groups ( t = 2.401, P = 0.020) (Figure 3 A). Furthermore, seasonal trend decomposition using locally weighted regression (STL) was applied to the R.tanezumi density data over the observation period. The STL analysis confirmed that the density of R.tanezumi exhibited significant seasonal fluctuations, which might be closely related to seasonal environmental changes such as temperature, precipitation, and the availability of food resources (Figure 3 B). Univariate analysis for screening the ecological drivers We conducted a comprehensive analysis using GAM with a quasi-Poisson link function to examine the relationship between density of R.tanezumi and environmental predictors. Taking the density of R.tanezumi as the response variable, and a total of 24 variables such as meteorological factors, NDVI, with a lag of 0 to 3 months as explanatory variables for univariate analysis. It was found that 16 explanatory variables had a significant effect on the density change of R.tanezumiat the P <0.05 level(Table 2). Table 2 . Hypothesis test results of univariate analysis on the density of R.tanezumi in the TGR area from 2015 to 2021 Predictor Lag (months) F-value P R 2 Deviance explained(%) GCV M0Avg_Temp 0 3.166 0.002** 0.062 8.24 0.497 M0Min_Temp 0 3.905 0.005** 0.041 5.02 0.501 M0Max_Temp 0 5.472 0.001** 0.053 6.14 0.495 M0Avg_RHU 0 6.049 0.014* 0.016 1.86 0.511 M0Precip 0 12.930 ＜0.001*** 0.036 3.9 0.501 M1Avg_RHU 1 3.088 0.012* 0.009 1.22 0.515 M2Avg_Temp 2 4.297 0.041* 0.034 4.37 0.514 M2Min_Temp 2 5.059 0.027* 0.041 5.11 0.511 M2Precip 2 7.782 0.006** 0.0208 2.38 0.509 M2NDVI 2 3.123 0.007** 0.046 5.93 0.501 M2Avg_RHU 2 4.528 0.034* 0.011 1.40 0.514 M3Avg_Temp 3 9.015 ＜0.001*** 0.053 5.70 0.493 M3Min_Temp 3 11.650 ＜0.001*** 0.057 6.15 0.491 M3Max_Temp 3 4.239 0.001** 0.055 6.60 0.495 M3Precip 3 6.107 ＜0.001*** 0.058 6.58 0.492 M3NDVI 3 20.090 ＜0.001*** 0.056 5.93 0.490 * P<0.05. ** P<0.01. *** P<0.001. Abbreviation: Avg_Temp=monthly average temperature; Max_Temp=monthly average maximum temperature; Min_Temp=monthly average minimum temperature; Precip=monthly cumulative precipitation; Avg_RHU=monthly average relative humidity; NDVI= normalized difference vegetation index; GCV=Generalized crossvalidation. Multivariate GAM fitting From the 13 indicators of monthly average temperature, monthly average maximum temperature, monthly average minimum temperature, monthly average humidity, monthly average precipitation and NDVI with a lag of 0 to 3 months mentioned in the above text (Table 2), following the principle of the largest percentage of deviation explanation and the smallest GCV, Six smooth items, namely M0Avg_Temp, M0RHU_Avg, M3Min_Temp, M3Max_Temp, M3_Precip and M3NDVI, were extracted. Then, considering the collinearity problem, we used a regressition-based approximation method to calculate the variance inflation factor (VIF) to evaluate the collinearity among smooth predictors in GAM. This method takes into account the nonlinear dependence introduced by smooth splines. It eliminates the multicollinearity indicators M3Min_Temp and M3Max_Temp with VIF > 5. Finally, the model was constructed with four smooth terms: M0Avg_Temp, M0Avg_RHU, M3Precip, and M3NDVI(R 2 =0.12, Deviance explained = 14%, GCV = 0.4438) (Fig 4). These findings highlight the influence of meteorological conditions on the R.tanezumi density. The proliferation of the R.tanezumi population has an optimal temperature range (about 20℃). The density of R.tanezumi was negatively correlated with the current month's average relative humidity and the precipitation lagged by three months. Additionally, which also exhibited a fluctuating downward trend as the NDVI index lagged by three months increased. Stratified GAM for Reservoir Impoundment and Drawdown Phases in the TGR area To explore the influence of hydrology along the Three Gorges Reservoir area on model construction, we analyzed three distinct periods: the impoundment period (from November of the previous year to March of the following year), the drawdown period (from May to September), and the complete annual cycle. We conducted a stratified analysis by partitioning the dataset according to these distinct hydrological periods. Separate (GAM) were developed to quantitatively assess the effects of meteorological variables and vegetation indices on R.tanezumi population density. The model specifications and results are presented in Table 3. Statistical analysis revealed that the impoundment period GAM achieved the highest deviance explained (17.9%), outperforming the drawdown period model in terms of explanatory power. Table 3. The formulas and performance of the GAM for different periods in the TGR area from 2015 to 2021 Model formulation Group Months R 2 Deviance explained(%) GCV n Y i,t =α+s 1 (M0Avg_Temp)+s 2 (M0Avg_RHU) +s 3 (M3Precip)+s 4 (M3NDVI)+ε Complete annual cycle Jan, Mar, May, Jul, Sep, Nov 0.12 14 0.444 321 Drawdown period May, Jul, Sep 0.077 13 0.426 159 Impoundment period Nov, Jan, Mar 0.146 17.9 0.468 162 Interactive effects of ecological drivers To evaluate the interactive effects of meteorological variables on R.tanezumi population dynamics, we incorporated two-way interaction terms between key climatic predictors in GAM. The interactions between monthly average temperature and relative humidity( P <0.01), as well as the cumulative precipitation lagged by 3 months and the NDVI( P <0.01), all influence the density of R.tanezumi . When the temperature is relatively low (5–10°C), the density of R.tanezumi is relatively low and shows little change with increasing relative humidity. However, when the temperature rises to above 15°C, the density of R.tanezumi begins to increase significantly, especially when the relative humidity is between 75% and 85%, where the density reaches a higher level. Furthermore, when the temperature increases further to 20–25°C, the density of R.tanezumi decreases under extreme relative humidity conditions (65–70% or 85–90%), but remains relatively high under moderate humidity levels (75–85%). This may suggest that R.tanezumi has a certain threshold for environmental humidity, and both excessively high and low humidity levels are unfavorable for its survival (Fig 5A). Additionally, there is a significant interaction between precipitation and the vegetation index. When precipitation is low (50–100 mm), the density of R.tanezumi is relatively low when the vegetation index is low (0.3–0.4), but increases when the vegetation index is high (0.6–0.7). This indicates that the growth status of vegetation is important for the survival and activity of R.tanezumi under arid conditions. As precipitation increases (100–150 mm), the density of R.tanezumi reaches the highest level (2.0%–2.5%) in areas with moderate vegetation index (0.4–0.6). This is likely because moderate precipitation promotes vegetation growth, providing abundant food resources and habitats for R.tanezumi . However, when precipitation further increases (150–200 mm), the density of R.tanezumi decreases in areas with a high vegetation index (0.6–0.7) (Fig 5B). Discussion In the context of the World Health Organization's (WHO) global "One Health" initiative, which underscores the intricate interplay among humans, animals, and the ecological environment(23, 24), the impact of global changes—such as climate change, land use alterations, and globalization—on the mutation frequency and transmission dynamics of vector-borne infectious diseases has emerged as a critical area of interest. The TGR area is characterized by a subtropical monsoon climate, marked by plentiful rainfall and lush vegetation, including a variety of crops that flourish along its banks(14). This environment is particularly conducive to the proliferation of R.tanezumi . Our study represents a pioneering effort to explore the impact of environmental determinants, including climatic conditions and landscape features, as well as the interplay among multiple factors, on the observed decline in the population density of these rodents within the region. By conducting a thorough analysis, we aim to offer a scientific foundation for the local management of rodent populations, enhance monitoring efforts, and improve the early detection of diseases transmitted by rats. This research is instrumental in formulating evidence-based strategies for public health and ecological conservation in the area(25). The investigation found that from 2015 to 2021, the density of R.tanezumi in the study site identified significant seasonal variations in the population density. The density of these rodents increases during the spring months, particularly in March and May, likely due to the rise in temperature, vegetation growth, and the consequent increase in food resources. This trend continues into the summer months, from June to August, where the density reaches its peak, potentially due to the abundance of food and favorable breeding conditions. Conversely, in the autumn months of September and November, the density shows a decline. These seasonal fluctuations are crucial for understanding the ecological requirements of R.tanezumi and for forecasting population shifts in response to climate change and alterations in land use patterns. This information is instrumental in devising rodent management strategies that take into account environmental factors. Particularly during months with elevated densities of R.tanezumi , the implementation of more assertive control measures may be warranted to mitigate potential risks associated with high rodent populations(26). Italian scholars have revealed the spatial differentiation among the environment, hosts, and vectors by integrating geographic information systems (GIS) and multi-criteria evaluation (MCE) methods, incorporating expert knowledge, mosquito vector distribution data, and host density information. This approach enabled the creation of a vector-borne risk map for Rift Valley Fever (RVF) in Italy(27). Such methodology provides a valuable framework for rodent-borne disease early-warning research. Understanding the distribution dynamics of R.tanezumi is critical for preventing and controlling the transmission of rodent-borne diseases. Genetic structure diversity studies of R.tanezumi in China have indicated a dispersal trend of the species from southern China or coastal routes toward the inland Yangtze River Basin(28). Notably, our monitoring results show that the density of R.tanezumi in the TGR area exhibits a random distribution pattern. However, the driving factors behind the distribution and migration of R.tanezumi still require further investigation. We investigated the impact of environmental factors on the population density of R.tanezumi by considering both immediate and lagged effects(29, 30). Among these factors, monthly average temperature and relative humidity significantly influenced the population density, likely by primarily affecting the activity levels of these rodents. The effect of temperature on the population density of R.tanezumi exhibited an inverted U-shaped curve, indicating the existence of an optimal range of temperature and humidity for their foraging and activities. This conclusion is corroborated to some extent by studies on the genomic expression and behavioral experiments of R.tanezumi , which have demonstrated their relatively poor adaptability to cold climates (31, 32). On the other hand, monthly average relative humidity exhibited a negative correlation with the population density of R.tanezumi . Relative humidity is also a critical factor affecting ectoparasites on rodents. Warm and humid environments facilitate the infection of rodents by ectoparasites such as chigger mites, which may be one of the mechanisms underlying the negative correlation between high humidity and R.tanezumi density(29, 33, 34). A negative correlation was observed between the population density of R.tanezumi and both three-month lagged precipitation and three-month lagged NDVI. The potential lag effects of environmental factors on vector organism population density are of critical significance for early warning of rodent infestations and the formulation of proactive management strategies(35). In Indonesia, the population of R.tanezumi has been found to exhibit a negative response to annual lagged precipitation(36). The negative correlation between NDVI and R.tanezumi density is more often attributed to urban expansion and land reclamation(8, 37), which reduce vegetation cover indices but provide more suitable living environments for R.tanezumi ， It is well known that the R.tanezumiis a semi-domestic rat that is highly adapted to the human living environment. The increase in the density of R.tanezumiis in this area may be related to the accelerated urbanization process and the expansion of the human activity area in the local area. These findings from the Generalized Additive Models (GAM) underscore the importance of considering multiple environmental factors when predicting and managing rodent populations, and the identified relationships can inform targeted management strategies(25, 38). While this study provides valuable insights, it acknowledges several critical limitations due to data constraints. First, our dataset comprises historical records from 2015 to 2021, which precludes the incorporation of the most recent data. Second, the meteorological raster data employed in our analysis were aggregated at the county level, potentially failing to align precisely with specific monitoring sites. This misalignment may overlook local microclimates, such as urban heat islands and variations in temperature and humidity within vegetated areas(39), thereby introducing measurement bias(40). Additionally, the operational dynamics of the Three Gorges Dam must be considered. The fluctuation in reservoir water levels creates a 30-meter drawdown zone between the elevations of 145 and 175 meters. This zone begins to fill annually from September to October, and the rising water levels may trigger rodent migration, thereby increasing local rodent density(41). Our surveillance indeed detected statistically significant differences in rodent density between the water storage and drainage periods. These complex processes may lead to underestimation or overestimation of the impact of meteorological factors on rodent habitats in our models. Despite the acknowledged limitations, our main findings remain robust. This study's findings robustly affirm the climatic adaptability of R.tanezumi population density within the Three Gorges Reservoir Area. The research particularly underscores the immediate impacts of temperature and humidity, as well as the delayed influences of precipitation and NDVI on the rodent population(42). These insights are pivotal for shaping policy decisions and enhancing public health preparedness strategies. Our results advocate for heightened vigilance and proactive measures, especially as we approach the month of May—a critical period that may see an increase in the geographical spread of diseases transmitted by R.tanezumi and other rodents. It is imperative to augment and disseminate active surveillance efforts to track the distribution of these diseases, thereby facilitating a preemptive response to any potential outbreaks(19, 43). By understanding the intricate relationship between climate factors and rodent populations, we can better anticipate and mitigate the risks associated with rodent-borne diseases, safeguarding public health and contributing to effective disease management in the region. Conclusions This study provides the first comprehensive analysis of the spatiotemporal distribution of R. tanezumi in the TGR area of Chongqing, China, in relation to climatic and environmental drivers. Our findings identify several key factors shaping its local population density, including the current month's average temperature and relative humidity, as well as three-month-lagged precipitation and NDVI. The period from March to May emerged as a critical window of elevated rodent activity, necessitating targeted surveillance. Against the backdrop of global climate change, this study highlights the urgency of strengthening rodent-borne disease prevention and control strategies in the region. Based on the empirical patterns observed, we recommend that local public health authorities intensify rodent monitoring and control measures each spring (March–May), especially when temperatures reach 15–20°C accompanied by moderate humidity (75–85%), with prioritized efforts in discrete spatial clusters showing significant negative spatial autocorrelation. Declarations Acknowledgments We are grateful to the local county-level CDC stations throughout the Three Gorges Reservoir Area for granting access to the long-term rodent-surveillance database and for their indispensable coordination of field sampling. Specifically, we thank the CDC teams of Fuling, Wanzhou, Kaizhou, Fengdu, Zhong County, Yunyang, Fengjie, and Wushan for their sustained collaboration and logistical support. Authors' Contributions Hansen Xiao, Qiyong Liu and Hengqing Ji conceived and designed the study. Hansen Xiao, Kun Su, Zheng Wang and Taotian Tu performed the experiments and collected the data. Hansen Xiao, Haoqiang Ji, Meng Shang analyzed the data. Hansen Xiao, Qiyong Liu and Hengqing Ji wrote the manuscript. Funding This research was sponsored by the Chongqing Medical Leading Talent Project (No.YXLJ202419); Chongqing Young and Middle-aged Medical High-end Talent Program (No.YXGD202402); Chongqing medical scientific research project(Joint project of Chongqing Health Commission and Science and Technology Bureau) (No.2025MSXM001). Ethics approval and consent to participate All methods were performed in accordance with the relevant guidelines and regulations. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Amin, R., Ar Salan, M. S., & Hossain, M. M. (2024). Measuring the impact of responsible factors on CO2 emission using generalized additive model (GAM). Heliyon , 10 (4). https://doi.org/10.1016/j.heliyon.2024.e25416 Barker, C. M., Chen, Y., Hou, W., & Dong, J. (2023). Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model. PLOS Neglected Tropical Diseases , 17 (7). https://doi.org/10.1371/journal.pntd.0010806 Blasdell, K. R., Morand, S., Laurance, S. G. W., Doggett, S. L., Hahs, A., Trinh, K., Perera, D., & Firth, C. (2022). Rats and the city: Implications of urbanization on zoonotic disease risk in Southeast Asia. Proceedings of the National Academy of Sciences, 119 (39). https://doi.org/10.1073/pnas.2112341119 Cadavid Restrepo, A. M., Yang, Y. R., McManus, D. P., Gray, D. J., Barnes, T. S., Williams, G. M., Soares Magalhães, R. J., Hamm, N. A. S., & Clements, A. C. A. (2018). Spatiotemporal patterns and environmental drivers of human echinococcoses over a twenty-year period in Ningxia Hui Autonomous Region, China. Parasites & Vectors , 11 (1). https://doi.org/10.1186/s13071-018-2693-z Caminade, C., McIntyre, K. M., & Jones, A. E. (2018). Impact of recent and future climate change on vector-borne diseases. Annals of the New York Academy of Sciences , 1436 (1), 157–173. https://doi.org/10.1111/nyas.13950 Cao, R., Zhang, M., Chen, Y., Hou, G., Liu, Q., Zhang, J., & Zhang, Y. (2024). The special adaptation to hypoxia facilitated the expansion of the Asian house rat (Rattus tanezumi) into Tibet but not other Rattus species. Integrative Zoology , 20 (3), 568–585. https://doi.org/10.1111/1749-4877.12829 CHANG Zhao Rui, LU Liang, MAO De Qiang, PAN Hui Ming, FENG Lian Gui, YANG Xiao Bing, L. F. F., HE Yuan Yuan, ZHANG Jing, & YANG Wei Zhong. (2016). Dynamics of Rodent and Rodent-borne Disease during Construction of the Three Gorges Reservoir from 1997 to 2012*. https://doi.org/10.3967/bes2016.024 Chen, J.-J., Lv, C.-L., Wang, T., Wang, Y.-H., Che, T.-L., Xu, Q., Hong, X.-G., Teng, A.-Y., Tian, S., Zhang, Y.-Y., Liu, M.-C., Wang, L.-P., Hay, S. I., Yang, Y., Fang, L.-Q., & Liu, W. (2025). Small mammals and associated infections in China: a systematic review and spatial modelling analysis. The Lancet Regional Health - Western Pacific , 54 . https://doi.org/10.1016/j.lanwpc.2024.101264 Chen, Y.-L., Guo, X.-G., Ding, F., Lv, Y., Yin, P.-W., Song, W.-Y., Zhao, C.-F., Zhang, Z.-W., Fan, R., Peng, P.-Y., Li, B., Chen, T., & Jin, D.-C. (2023). Infestation of Oriental House Rat (Rattus tanezumi) with Chigger Mites Varies along Environmental Gradients across Five Provincial Regions of Southwest China. International Journal of Environmental Research and Public Health , 20 (3). https://doi.org/10.3390/ijerph20032203 Chen, Y., Hou, G., Jing, M., Teng, H., Liu, Q., Yang, X., Wang, Y., Qu, J., Shi, C., Lu, L., Zhang, J., & Zhang, Y. (2021). Genomic analysis unveils mechanisms of northward invasion and signatures of plateau adaptation in the Asian house rat. Molecular Ecology , 30 (24), 6596–6610. https://doi.org/10.1111/mec.16194 Ding, F. Y., Ge, H. H., Ma, T., Wang, Q., Hao, M. M., Li, H., Zhang, X. A., Maude, R. J., Wang, L. P., Jiang, D., Fang, L. Q., & Liu, W. (2023). Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China. Global Change Biology , 29 (23), 6647–6660. https://doi.org/10.1111/gcb.16969 Elliott, I., Thangnimitchok, N., Chaisiri, K., Wangrangsimakul, T., Jaiboon, P., Day, N. P. J., Paris, D. H., Newton, P. N., & Morand, S. (2021). Orientia tsutsugamushi dynamics in vectors and hosts: ecology and risk factors for foci of scrub typhus transmission in northern Thailand. Parasites & Vectors , 14 (1). https://doi.org/10.1186/s13071-021-05042-4 Heit, D. R., Ortiz-Calo, W., Poisson, M. K. P., Butler, A. R., & Moll, R. J. (2024). Generalized nonlinearity in animal ecology: Research, review, and recommendations. Ecology and Evolution , 14 (7). https://doi.org/10.1002/ece3.11387 Hewitt, J., Zhao, Y., Ge, L., Zhou, Y., Sun, Z., Zheng, E., Wang, X., Huang, Y., & Cheng, H. (2018). A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS). Plos One , 13 (11). https://doi.org/10.1371/journal.pone.0207518 Htwe, N. M., Sudarmaji, Pustika, A. B., Brown, P. R., Stuart, A., Duque, U., Singleton, G. R., & Jacob, J. (2024). Impacts of rainfall and rainfall anomalies on the population dynamics of rodents in southeast Asian rice fields. Pest Management Science , 80 (11), 5574–5583. https://doi.org/10.1002/ps.8260 Huang, L.-Q., Guo, X.-G., Speakman, J. R., & Dong, W.-G. (2013). Analysis of gamasid mites (Acari: Mesostigmata) associated with the Asian house rat, Rattus tanezumi (Rodentia: Muridae) in Yunnan Province, Southwest China. Parasitology Research , 112 (5), 1967–1972. https://doi.org/10.1007/s00436-013-3354-y Jiang, F., Wang, L., Wang, S., Zhu, L., Dong, L., Zhang, Z., Hao, B., Yang, F., Liu, W., Deng, Y., Zhang, Y., Ma, Y., Pan, B., Han, Y., Ren, H., & Cao, G. (2019). Meteorological factors affect the epidemiology of hemorrhagic fever with renal syndrome via altering the breeding and hantavirus-carrying states of rodents and mites: a 9 years’ longitudinal study. Emerging Microbes & Infections , 6 (1), 1–9. https://doi.org/10.1038/emi.2017.92 Jonathan L. Richardson, Elizabeth P. McCoy, Nicholas Parlavecchio, Ryan Szykowny, Eli Beech- Brown, Jan A. Buijs, Jacqueline Buckley, Robert M. Corrigan, Federico Costa, Ray DeLaney, Rachel Denny, Leah Helms, Wade Lee, Maureen H. Murray, Claudia Riegel, Fabio N. Souza, John Ulrich, Adena Why, & Kiyokawa, Y. (2025). Increasing rat numbers in cities are linked to climate warming, urbanization, and human population. Ke Li, Zhenxu Wang, Xiaohui Wei, Haoqiang Ji, Meng Shang, Nan Chang, Zihao Wang, Chenran Guo, Lei Xu, Ning Zhao, & Liu, Q. (2024). Relationships Between Meteorological Factors and Mongolian Gerbils and Its Flea Burdens — Xilingol League, Inner Mongolia Autonomous Region, China, 2012–2021. https://doi.org/doi: 10.46234/ccdcw2024.107 Kosoy, M., Khlyap, L., Cosson, J.-F., & Morand, S. (2015). Aboriginal and Invasive Rats of GenusRattusas Hosts of Infectious Agents. Vector-Borne and Zoonotic Diseases , 15 (1), 3–12. https://doi.org/10.1089/vbz.2014.1629 Kouroupis, D., Charisi, K., & Pyrpasopoulou, A. (2023). The Ongoing Epidemic of West Nile Virus in Greece: The Contribution of Biological Vectors and Reservoirs and the Importance of Climate and Socioeconomic Factors Revisited. Tropical Medicine and Infectious Disease , 8 (9). https://doi.org/10.3390/tropicalmed8090453 Kreppel, K. S., Telfer, S., Rajerison, M., Morse, A., & Baylis, M. (2016). Effect of temperature and relative humidity on the development times and survival of Synopsyllus fonquerniei and Xenopsylla cheopis, the flea vectors of plague in Madagascar. Parasites & Vectors , 9 (1). https://doi.org/10.1186/s13071-016-1366-z Kulkarni, M. A., Berrang-Ford, L., Buck, P. A., Drebot, M. A., Lindsay, L. R., & Ogden, N. H. (2019). Major emerging vector-borne zoonotic diseases of public health importance in Canada. Emerging Microbes & Infections , 4 (1), 1–7. https://doi.org/10.1038/emi.2015.33 Li, J., Huang, E., Wu, Y., Zhu, C., Li, W., Ai, L., Xie, Q., Tian, Z., Zhong, W., Sun, G., Zhang, L., & Tan, W. (2024). Population structure, dispersion patterns and genetic diversity of two major invasive and commensal zoonotic disease hosts (Rattus norvegicus and Rattus tanezumi) from the southeastern coast of China. Frontiers in Genetics, 14 . https://doi.org/10.3389/fgene.2023.1174584 Li, K., Chen, Y., & Jiang, J. (2023). Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities. Environment International , 180 . https://doi.org/10.1016/j.envint.2023.108196 Li, K., Sommer, S., Yang, Z., Guo, Y., Yue, Y., Ozgul, A., & Wang, D. (2022). Distinct body-size responses to warming climate in three rodent species. Proceedings of the Royal Society B: Biological Sciences, 289 (1972). https://doi.org/10.1098/rspb.2022.0015 Liu, R.-J., Guo, X.-G., Peng, P.-Y., Lv, Y., Yin, P.-W., Song, W.-Y., Xiang, R., Chen, Y.-L., Li, B., & Jin, D.-C. (2025). Mite Infestation on Rattus tanezum rats in southwest China concerning risk models. Frontiers in Veterinary Science , 12 . https://doi.org/10.3389/fvets.2025.1519188 Liu, R.-J., Guo, X.-G., Zhao, C.-F., Zhao, Y.-F., Peng, P.-Y., & Jin, D.-C. (2024). An Ecological Survey of Chiggers (Acariformes: Trombiculidae) Associated with Small Mammals in an Epidemic Focus of Scrub Typhus on the China–Myanmar Border in Southwest China. Insects , 15 (10). https://doi.org/10.3390/insects15100812 Loan, H. K., Van Cuong, N., Takhampunya, R., Kiet, B. T., Campbell, J., Them, L. N., Bryant, J. E., Tippayachai, B., Van Hoang, N., Morand, S., Hien, V. B., & Carrique-Mas, J. J. (2015). How Important Are Rats As Vectors of Leptospirosis in the Mekong Delta of Vietnam? Vector-Borne and Zoonotic Diseases , 15 (1), 56–64. https://doi.org/10.1089/vbz.2014.1613 Madelynn H. Howard, Christie M. Sayes, John P. Giesy, & a, Y. L. (2024). Valley fever under a changing climate in the United States. https://doi.org/10.1016/j.envint.2024.109066 Munoz-Zanzi, C., Talmoudi, K., Bellali, H., Ben-Alaya, N., Saez, M., Malouche, D., & Chahed, M. K. (2017). Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009–2015: Forecasting models using climate variables as predictors. PLOS Neglected Tropical Diseases , 11 (8). https://doi.org/10.1371/journal.pntd.0005844 Ravindra, K., Rattan, P., Mor, S., & Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment International , 132 . https://doi.org/10.1016/j.envint.2019.104987 Sandy, J., Uea-Anuwong, T., Kiu, L. H., Lee, L. K. F., Abdullah, S., & Magouras, I. (2024). Capillaria hepatica (syn. Calodium hepaticum) infection and factors influencing infection carriage in rats (Rattus spp.) in Hong Kong. One Health , 19 . https://doi.org/10.1016/j.onehlt.2024.100878 Sehgal, A., Mehta, S., Sahay, K., Martynova, E., Rizvanov, A., Baranwal, M., Chandy, S., Khaiboullina, S., Kabwe, E., & Davidyuk, Y. (2023). Hemorrhagic Fever with Renal Syndrome in Asia: History, Pathogenesis, Diagnosis, Treatment, and Prevention. Viruses , 15 (2). https://doi.org/10.3390/v15020561 Shaheen, M. N. F. (2022). The concept of one health applied to the problem of zoonotic diseases. Reviews in Medical Virology , 32 (4). https://doi.org/10.1002/rmv.2326 Swamy, G. S. N. V. K. S. N., Nagendra, S. M., & Schlink, U. (2020). Impact of urban heat island on meteorology and air quality at microenvironments. Journal of the Air & Waste Management Association , 70 (9), 876–891. https://doi.org/10.1080/10962247.2020.1783390 Thomson, M. C., Solomon, C. G., Salas, R. N., & Stanberry, L. R. (2022). Climate Change and Vectorborne Diseases. New England Journal of Medicine , 387 (21), 1969–1978. https://doi.org/10.1056/NEJMra2200092 Xiaojun Wua, Lunche Wang, Qian Cao, Zigeng Niu, & Xin Dai. (2023). Regional climate change and possible causes over the Three Gorges Reservoir Area. https://doi.org/10.1016/j.scitotenv.2023.166263 Zhang, M.-Y., Cao, R.-D., Chen, Y., Ma, J.-C., Shi, C.-M., Zhang, Y.-F., Zhang, J.-X., Zhang, Y.-H., & Su, B. (2024). Genomic and Phenotypic Adaptations ofRattus tanezumito Cold Limit Its Further Northward Expansion and Range Overlap withR. norvegicus. Molecular Biology and Evolution , 41 (6). https://doi.org/10.1093/molbev/msae106 Zhang, X., Peng, L., Liu, W., Fan, L., Zhang, Q., Chen, G., He, P., Wu, R., Liu, A., Yang, Y., Yu, X., & Yun, L. (2014). Response of primary vectors and related diseases to impoundment by the Three Gorges Dam. Tropical Medicine & International Health , 19 (4), 440–449. https://doi.org/10.1111/tmi.12272 Zhao, W., Zhou, H., Yang, L., Ma, T., Zhou, J., Liu, H., Lu, G., & Huang, H. (2020). Prevalence, genetic diversity and implications for public health of Enterocytozoon bieneusi in various rodents from Hainan Province, China. Parasites & Vectors , 13 (1). https://doi.org/10.1186/s13071-020-04314-9 Zinsstag, J., Crump, L., Schelling, E., Hattendorf, J., Maidane, Y. O., Ali, K. O., Muhummed, A., Umer, A. A., Aliyi, F., Nooh, F., Abdikadir, M. I., Ali, S. M., Hartinger, S., Mäusezahl, D., de White, M. B. G., Cordon-Rosales, C., Castillo, D. A., McCracken, J., Abakar, F., Cercamondi, C., Emmenegger, S., Maier, E., Karanja, S., Bolon, I., de Castañeda, R. R., Bonfoh, B., Tschopp, R., Probst-Hensch, N., & Cissé, G. (2018). Climate change and One Health. FEMS Microbiology Letters , 365 (11). https://doi.org/10.1093/femsle/fny085 Zuo, L., Wang, H., Tan, Y., Wan, J., Tan, W., Gan, Y., Xiong, X., Wang, J., & Luo, C. (2024). Co-circulation of Hantavirus, Pathogenic Leptospira spp., and Bartonella spp. in Rodents in the Wanzhou Section of the Three Gorges Reservoir Region, 2021–2023. Vector-Borne and Zoonotic Diseases , 24 (10), 694–698. https://doi.org/10.1089/vbz.2023.0150 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Submission checks completed at journal 04 Nov, 2025 First submitted to journal 18 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7896124","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540091695,"identity":"7a1b02e4-fce3-48c1-a21c-b51ec943793c","order_by":0,"name":"Hansen Xiao","email":"","orcid":"","institution":"Chongqing Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Hansen","middleName":"","lastName":"Xiao","suffix":""},{"id":540091696,"identity":"c7a5a091-aae0-4131-a060-2fce6ac0da8d","order_by":1,"name":"Qiyong Liu","email":"","orcid":"","institution":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qiyong","middleName":"","lastName":"Liu","suffix":""},{"id":540091697,"identity":"5487a1f2-8217-477d-a947-5ac7b9f53341","order_by":2,"name":"Kun Su","email":"","orcid":"","institution":"Chongqing Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Su","suffix":""},{"id":540091698,"identity":"6cc9ef0a-00b8-4c06-a257-ac3c2bb12e1c","order_by":3,"name":"Zheng Wang","email":"","orcid":"","institution":"Chongqing Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""},{"id":540091699,"identity":"abdabd6a-9d56-4e32-8818-b939bd4632c9","order_by":4,"name":"Taotian Tu","email":"","orcid":"","institution":"Chongqing Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Taotian","middleName":"","lastName":"Tu","suffix":""},{"id":540091700,"identity":"1e376bd6-42f7-4203-b4d1-4f1ff9e661c0","order_by":5,"name":"Jing Wei","email":"","orcid":"","institution":"Chongqing Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wei","suffix":""},{"id":540091701,"identity":"44275315-8aad-4d5a-8c45-86ea77225d64","order_by":6,"name":"Haoqiang Ji","email":"","orcid":"","institution":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Haoqiang","middleName":"","lastName":"Ji","suffix":""},{"id":540091702,"identity":"377f6c0f-12df-41b5-9e7c-9cf6ba19a28a","order_by":7,"name":"Meng Shang","email":"","orcid":"","institution":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Shang","suffix":""},{"id":540091703,"identity":"d6732679-6db3-4a5a-8ea9-4313842683fe","order_by":8,"name":"Hengqing Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAxjJ2Mx84MCHH6RpYUs8OLOHaC1gwGN8mIONCC3m7L2HX7wpuGPX3M7z4TADD4M8v9gB/Fose86lWc4xeJbc2My74XCBBYPhzNkJBBx2I8fMmMfgcDIjSMsMHoYEg9uEtNx/A9PC8+AwDxsxWm7wGD8GarEDamEgUsuZHDPGOQaHE4CBbAAMZAki/HL8jPGHN38O2xv2H3784cMPG3l+aQJagIBNgoeBIXFjA5gjQVA5CDB/AGqxlydK7SgYBaNgFIxIAAASTEgMWYc3bwAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Hengqing","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2025-10-19 03:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7896124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7896124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98628427,"identity":"76dc46a9-bcf8-4a4e-915e-d78af07bea07","added_by":"auto","created_at":"2025-12-19 17:11:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of surveillance site in the TGR area of Chongqing from 2015 to 2021. \u003c/strong\u003eThe eight reservoir-district counties, situated in the northeastern part of Chongqing Municipality, encompass surveillance sites distributed along the Yangtze River and its adjacent tributaries\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7896124/v1/ad4ae2afc5268ba730791ba7.png"},{"id":98615646,"identity":"f0e5e828-a131-4d17-a293-478dca599904","added_by":"auto","created_at":"2025-12-19 15:12:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage density distribution of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eR.tanezumi\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the TGR area of Chongqing from 2015 to 2021. \u003c/strong\u003eThe density of \u003cem\u003eR.tanezumi\u003c/em\u003e showed a gradually increasing trend from 2015 to 2021. The Moran's I index and corresponding p-values for each year were as follows: in 2015, Moran's I index = -0.022, \u003cem\u003eP \u003c/em\u003e= 0.739; in 2016, Moran's I index = 0.074, \u003cem\u003eP\u003c/em\u003e = 0.470; in 2017, Moran's I index = -0.343, \u003cem\u003eP\u003c/em\u003e = 0.564; in 2018, Moran's I index = -0.903, \u003cem\u003eP \u003c/em\u003e= 0.030; in 2019, Moran's I index = -0.654, \u003cem\u003eP \u003c/em\u003e= 0.167; in 2020, Moran's I index = -0.831,\u003cem\u003e P\u003c/em\u003e = 0.033; in 2021, Moran's I index = 0.117, \u003cem\u003eP \u003c/em\u003e= 0.472.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7896124/v1/4de02120e3003ad5761f49fa.png"},{"id":98629433,"identity":"9c82b56b-5033-44f9-be45-034ace45834c","added_by":"auto","created_at":"2025-12-19 17:13:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal changes in the population density of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eR.tanezumi\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the TGR area of Chongqing from 2015 to 2021.\u003c/strong\u003e (A) There were statistically significant differences in Monthly survey results of the density of \u003cem\u003eR.tanezumi\u003c/em\u003e ((\u003cem\u003eF\u003c/em\u003e= 3.001, \u003cem\u003eP\u003c/em\u003e = 0.021), additionally, significant statistical differences were observed in the density of \u003cem\u003eR.tanezumi \u003c/em\u003ebetween the impoundment period and the drainage period(\u003cem\u003et\u003c/em\u003e= 2.401, \u003cem\u003eP\u003c/em\u003e= 0.020);(B)STL Time series decomposition of the density of \u003cem\u003eR.tanezumi\u003c/em\u003e confirmed that the density of \u003cem\u003eR.tanezumi\u003c/em\u003e exhibited significant seasonal fluctuations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7896124/v1/09a73bc76dc1bba3334beb3b.png"},{"id":98615649,"identity":"cbd34c02-38bc-42b2-9b68-fa845a0586c9","added_by":"auto","created_at":"2025-12-19 15:12:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of factors influencing the density of\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e R.tanezumi\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the TGR area from 2015 to\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2021 using generalized additive modeling.\u003c/strong\u003e \u0026nbsp;(A) The correlation between density of\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eR.tanezumi\u003c/em\u003e and the monthly average temperature; (B) The association between density of\u003cem\u003e R.tanezumi\u003c/em\u003e and\u003c/p\u003e\n\u003cp\u003ethe monthly average relative humidity; (C) The link between between density of \u003cem\u003eR.tanezumi \u003c/em\u003eand the\u003c/p\u003e\n\u003cp\u003emonthly cumulative precipitation with a 3-month \u0026nbsp;lag; (D) The connection between density of\u003cem\u003e R.tanezumi\u003c/em\u003e and monthly NDVI with a 3-month lag.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7896124/v1/e53463f94bf23def511bc4fd.png"},{"id":98615648,"identity":"a3fa30b8-76f5-44cf-82e7-0dc9e0b37a7b","added_by":"auto","created_at":"2025-12-19 15:12:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe interaction effect of the monthly average temperature and monthly average relative humidity of the current month, the cumulative precipitation lagging by 3 months and the NDVI lagging by 3 months on the density of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eR.tanezumi\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in the TGR area from 2015 to 2021. \u003c/strong\u003e(A) The interactions between monthly average temperature and relative humidity for the density of\u003cem\u003e R.tanezumi \u003c/em\u003e(R\u003csup\u003e2\u003c/sup\u003e=0.131, Deviance explained=17.3%, GCV=0.448); (B) the cumulative precipitation lagged by 3 months and the normalized difference vegetation index (NDVI) for the density of\u003cem\u003e R.tanezumi\u003c/em\u003e(R\u003csup\u003e2\u003c/sup\u003e=0.125, Deviance explained =15.3%, GCV=0.439).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7896124/v1/eb2c0e4fd9f48b7a5c3acd80.png"},{"id":98775063,"identity":"8ba3196f-7ad6-46cb-8e61-618c64378993","added_by":"auto","created_at":"2025-12-22 12:18:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1694434,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7896124/v1/43089aa2-99bd-4c90-92c4-9377bc0f2615.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal dynamics of Rattus tanezumi density and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China","fulltext":[{"header":"Background","content":"\u003cp\u003eThe \u003cem\u003eR.tanezumi\u003c/em\u003e, also called Asian house rat (1), is a highly adaptable murid rodent that has established a commensal relationship with human populations across tropical and subtropical Asia(2, 3). \u003cem\u003eR.tanezumi\u003c/em\u003e serves as a host and vector for numerous pathogens(4). It can spread a wide range of vector-borne diseases and zoonoses, including plague, hemorrhagic fever with renal syndrome, leptospirosis, through direct contact, the carriage of parasites, and fecal contamination(5-7). In addition, it’s also the main pest in farmland in the word, that causes damage to crops in spring and autumn before harvest(8, 9). Due to its wide geographical distribution, high density and frequent interaction with humans, it poses a major threat to human health(10, 11).\u003c/p\u003e\n\u003cp\u003eThe Three Gorges project is one of the largest hydropower projects in the world. It is in the Yangtze River Basin of China and involves more than 20 districts and counties in Chongqing. The TGR area has a unique ecological background, mild and humid climate, rich water resources and complex ecosystem, which provide suitable conditions for the breeding and disease transmission of vectors such as \u003cem\u003eR.tanezumi\u003c/em\u003e(12). It is reported that in the investigation of rodents in the Chongqing section of the TGR area, pathogenic agents such as Hantavirus, pathogenic Leptospira, and Bartonella were found to be carried(13). In addition, the construction of the Three Gorges Project, large-scale human activities, and climate change have led to significant alterations in the ecological environment of the reservoir area(14). After the completion of the TGR, the water level for winter storage and power generation is maintained at 175 m, while during the summer flood season, the water level is lowered to 145 m for flood control. With the periodic fluctuations in the reservoir water level, when water storage commences and the water level rises, rodents may migrate upward in response to the rising water level, potentially causing a local increase in rodent density and thereby posing a risk of rodent-borne diseases. This may alter the distribution, behavior, and population dynamics of \u003cem\u003eR.tanezumi\u003c/em\u003e, subsequently changing its disease transmission potential and having cascading effects on the ecosystem and public health. Therefore, it is imperative to emphasize the development of surveillance, early warning, and prevention strategies for vector-borne diseases associated with \u003cem\u003eR.tanezumi\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eMany studies have shown that the population composition, density, seasonal fluctuation and other ecological characteristics of vectors are closely related to the epidemic intensity of related diseases in the population(15-17). At the same time, these ecological characteristics of rodents, including \u003cem\u003eR.tanezumi\u003c/em\u003e, are profoundly affected by meteorological factors such as temperature, precipitation, humidity and extreme weather events, and have also been recognized by the academic community(18-20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the specific drivers and their lagged effects on\u003cem\u003e\u0026nbsp;R. tanezumi\u0026nbsp;\u003c/em\u003epopulations within the unique context of the TGR's water-level fluctuation zone remain poorly quantified. In this study, we used the surveillance data of \u003cem\u003eR.tanezumi\u003c/em\u003e density in eight districts and counties along the TGR Area in Chongqing from 2015 to 2021, to analyze the spatial-temporal changes and spatial correlation of \u003cem\u003eR.tanezumi\u003c/em\u003e density through GIS system. Then, we explore the association between the \u003cem\u003eR.tanezumi\u003c/em\u003e density and six explanatory variables (mainly involving climate and land cover) by using a generalized additive model (GAM)(21). This model can provide a reference for the study of the spatiotemporal dynamics of \u003cem\u003eR.tanezumi\u0026nbsp;\u003c/em\u003eunder future climate and land cover changes, thereby contributing to the prevention and control of local rodent-borne diseases(22).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSetting of surveillance site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2015 to 2021, the surveillance was carried out in eight districts or counties along the Chongqing section of the TGR area, namely Fuling, Wanzhou, Kaizhou, Fengdu, Zhong County, Yunyang, Fengjie, and Wushan. Each district or county selected two resettlement villages or towns less than 1 km away from the shoreline of the Yangtze river or its tributaries as surveillance site(Fig 1). The density of \u003cem\u003eR.tanezumi\u003c/em\u003e was surveillanced in January, March, May, July, September and November each year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods for Assessing \u003cem\u003eR.tanezumi\u003c/em\u003e density\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;cage trapping method with marinated pork (skin area of 1 cm\u0026times;1 cm) as bait was used for the surveillance. Each survey lasted for 3 days, 100 mousetrap cages were placed in the indoor and cultivated land respectively very day. Indoor, mousetrap cage is placed every 15 square meters on average. In the cultivated land, mousetrap cage is placed every 5 meters along the line, and the row spacing of the cages were 50 meters. In addition, the cage is placed in the late and collected in the morning. The monthly density of\u003cem\u003e\u0026nbsp;R.tanezumi\u003c/em\u003e was calculated by dividing the number of captured \u003cem\u003eR.tanezumi\u003c/em\u003e by the number of effective cages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources of ecological environment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHistorical climate data and land cover data were collated on a monthly basis, including monthly average temperature(Avg_Temp), monthly average maximum temperature(Max_Temp), monthly average minimum temperature(Min_Temp), monthly cumulative precipitation(Precip), monthly average relative humidity(Avg_RHU), and monthly Normalized Difference Vegetation Index(NDVI), which were obtained from the website of Resource and Environmental Science Data Center, China(https://www.resdc.cn\u0026nbsp;). In addition, the above data from differing grid geometries were aggregated to district or county level by using ArcGIS 10.7 software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModeling the density dynamics of \u003cem\u003eR.tanezumi\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GAM that has been successfully used to simulate the population density dynamics of some vectors was performed to explore the associations between monthly the density of \u003cem\u003eR.tanezumi\u003c/em\u003e and above explanatory variables. Considering the possible lag effect of explanatory variables on response variables, this study conducted a lag of 0-3 months for explanatory variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, in the R language programming environment, the \u0026quot;GAM\u0026quot; function in the \u0026quot;mgcv\u0026quot; package of r4.2.2 was used, with the density of \u003cem\u003eR.tanezumi\u003c/em\u003e as the response variable and meteorological and NDVI data with a lag of 0-3 months as the explanatory variables to establish the GAM model. Firstly, significant explanatory variables were screened through single-factor analysis (test level \u003cem\u003e\u0026alpha;\u003c/em\u003e=0.05), and generalized cross-validation (GCV), coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), and explanatory bias (DE) were used as the model evaluation criteria, the smoothing parameter (k) for each term was automatically selected by the mgcv package using the REML method. Variables with statistical significance, lower GCV, higher R\u003csup\u003e2\u003c/sup\u003e and higher DE were included in GAM for multivariate analysis. Meanwhile, collinearity diagnosis was conducted for the meaningful variables in the multivariate analysis and they were included in the interaction model for analysis. Collinearity diagnosis was conducted by calculating the variance inflation factor (VIF) of each explanatory variable. The GAM model can be written as follows.\u003c/p\u003e\n\u003cp\u003eY\u003csub\u003ei,t\u003c/sub\u003e=\u0026alpha;+s\u003csub\u003e1\u003c/sub\u003e(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003e1\u003c/sub\u003e)+s\u003csub\u003e2\u003c/sub\u003e(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003e2\u003c/sub\u003e) +s\u003csub\u003e3\u003c/sub\u003e(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003e3\u003c/sub\u003e)+\u0026hellip;+s\u003csub\u003en\u003c/sub\u003e(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003en\u003c/sub\u003e)+\u0026epsilon;\u003c/p\u003e\n\u003cp\u003ewhere i is the region, t the time (month), and s( ) means a smoothing function. Dependent variable Y\u003csub\u003ei, t\u003c/sub\u003e refers to the density of \u003cem\u003eR.tanezumi\u003c/em\u003e in region i at time t (month). Variable l refers to the lagged month(0,1,2,3), The independent variable M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003en\u003c/sub\u003e refers to the explanatory factors(Avg_Temp, Max_Temp, Min_Temp, Precip, Avg_RHU, NDVI) in region i and lag l month.\u003c/p\u003e\n\u003cp\u003eThe tensor product smoothing function te () was used to fit the influence of the interaction of the main meteorological factors on the mouse density, and the results were presented as a smooth surface. The basic model is as follows.\u003c/p\u003e\n\u003cp\u003eY\u003csub\u003ei,t\u003c/sub\u003e=\u0026alpha;+te(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003e1\u003c/sub\u003e, M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003e2\u003c/sub\u003e) +s\u003csub\u003e3\u003c/sub\u003e(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003e3\u003c/sub\u003e)+\u0026hellip;+s\u003csub\u003en\u003c/sub\u003e(M\u003csub\u003ei,l\u003c/sub\u003e X\u003csub\u003en\u003c/sub\u003e)+\u0026epsilon;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal distribution of \u003cem\u003eR.tanezumi\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Brown-Forsythe test confirmed the homogeneity of density values in various regions, thereby verifying the use of parametric analysis of variance. Among them, regional differences are significant(\u003cem\u003eF\u003c/em\u003e=3.772, \u003cem\u003eP\u003c/em\u003e=0.003). However, the Mann-Kendall trend test showed no significant temporal trend in density variation (\u0026tau; = 0.143, \u003cem\u003eP\u003c/em\u003e= 0.764)(Table 1). Spatial heterogeneity analysis using Moran\u0026apos;s I index revealed predominantly random distribution patterns of \u003cem\u003eR.tanezumi\u003c/em\u003e density across eight counties. However, significant negative spatial autocorrelation emerged in 2018 (Moran\u0026apos;s I=-0.90, \u003cem\u003eP\u003c/em\u003e=0.03) and 2020 (Moran\u0026apos;s I = -0.83, \u003cem\u003eP\u003c/em\u003e = 0.03), indicating a dispersed distribution where high-density clusters were adjacent to low-density areas (Fig 2). This checkerboard pattern suggests localized population regulation mechanisms during these years.\u0026nbsp;It should be noted that due to COVID-19 pandemic restrictions, longitudinal monitoring of R.tanezumi populations in Wushan and Zhong Counties was interrupted in 2020, resulting in missing annual density records. These data gaps were addressed using adjacent-year mean imputation (AYMI), whereby missing 2020 values were replaced with the arithmetic mean of corresponding 2019 and 2021 measurements for each county.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u0026nbsp;Density of \u003cem\u003eR.tanezumi\u003c/em\u003e in the TGR area from 2015 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"741\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 621px;\"\u003e\n \u003cp\u003eDensity of\u003cem\u003e\u0026nbsp;R.tanezumi\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003eTotal Density(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eWanzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eFuling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eKaizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eFengdu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eFengjie\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWushan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eZhongxian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eYunyang\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.56\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Missing data imputed using adjacent-year mean imputation method.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The D\u0026apos;Agostino \u0026amp; Pearson test was conducted to assess the homogeneity of variances across months for mouse density data, and the results confirmed the assumption of equal variances, thereby validating the appropriateness of employing one-way ANOVA for analyzing \u003cem\u003eR.tanezumi\u003c/em\u003e density in different months. The one-way ANOVA results revealed significant monthly variations in mouse density (\u003cem\u003eF\u003c/em\u003e = 3.001, \u003cem\u003eP\u003c/em\u003e = 0.021). Specifically, the density of \u003cem\u003eR.tanezumi\u003c/em\u003e was relatively low in January, and it gradually increased in March and May as spring approached. By autumn (November), the density began to decline. Additionally, an independent samples t-test was performed to compare \u003cem\u003eR.tanezumi\u003c/em\u003e density between the two groups of the impoundment period (January, March, November) and the discharge period (May, July, September) of the TGR. The results showed a statistically significant difference in \u003cem\u003eR.tanezumi\u003c/em\u003e density between these two groups (\u003cem\u003et\u0026nbsp;\u003c/em\u003e= 2.401, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.020) (Figure 3 A). Furthermore, seasonal trend decomposition using locally weighted regression (STL) was applied to the \u003cem\u003eR.tanezumi\u003c/em\u003e density data over the observation period. The STL analysis confirmed that the density of \u003cem\u003eR.tanezumi\u003c/em\u003e exhibited significant seasonal fluctuations, which might be closely related to seasonal environmental changes such as temperature, precipitation, and the availability of food resources (Figure 3 B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis for screening the ecological drivers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a comprehensive analysis using GAM with a quasi-Poisson link function to examine the relationship between density of\u003cem\u003e\u0026nbsp;R.tanezumi\u003c/em\u003e and environmental predictors. Taking the density of\u003cem\u003e\u0026nbsp;R.tanezumi\u003c/em\u003e as the response variable, and a total of 24 variables such as meteorological factors, NDVI, with a lag of 0 to 3 months as explanatory variables for univariate analysis. It was found that 16 explanatory variables had a significant effect on the density change of \u003cem\u003eR.tanezumiat\u003c/em\u003e the\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.05 level(Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e. Hypothesis test results of univariate analysis on the density of \u003cem\u003eR.tanezumi\u003c/em\u003e in the TGR area\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efrom 2015 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLag (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eDeviance explained(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGCV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM0Avg_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM0Min_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM0Max_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e5.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM0Avg_RHU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e6.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM0Precip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e＜0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM1Avg_RHU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e3.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM2Avg_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM2Min_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e5.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM2Precip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.0208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM2NDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM2Avg_RHU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM3Avg_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e9.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e＜0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM3Min_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e11.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e＜0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM3Max_Temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM3Precip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e6.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e＜0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eM3NDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e＜0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*\u0026nbsp;P\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e**\u0026nbsp;P\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003e***\u0026nbsp;P\u0026lt;0.001.\u003c/p\u003e\n\u003cp\u003eAbbreviation: Avg_Temp=monthly average temperature; Max_Temp=monthly average maximum temperature; Min_Temp=monthly average minimum temperature; Precip=monthly cumulative precipitation; Avg_RHU=monthly average relative humidity; NDVI= normalized difference vegetation index; GCV=Generalized crossvalidation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate GAM\u003c/strong\u003e \u003cstrong\u003efitting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the 13 indicators of monthly average temperature, monthly average maximum temperature, monthly average minimum temperature, monthly average humidity, monthly average precipitation and NDVI with a lag of 0 to 3 months mentioned in the above text (Table 2), following the principle of the largest percentage of deviation explanation and the smallest GCV, Six smooth items, namely M0Avg_Temp, M0RHU_Avg, M3Min_Temp, M3Max_Temp, M3_Precip and M3NDVI, were extracted. Then, considering the collinearity problem, we used a regressition-based approximation method to calculate the variance inflation factor (VIF) to evaluate the collinearity among smooth predictors in GAM. This method takes into account the nonlinear dependence introduced by smooth splines. It eliminates the multicollinearity indicators M3Min_Temp and M3Max_Temp with VIF \u0026gt; 5. Finally, the model was constructed with four smooth terms: M0Avg_Temp, M0Avg_RHU, M3Precip, and M3NDVI(R\u003csup\u003e2\u003c/sup\u003e =0.12, Deviance explained = 14%,\u0026nbsp;GCV = 0.4438) (Fig 4).\u003c/p\u003e\n\u003cp\u003eThese findings highlight the influence of meteorological conditions on the \u003cem\u003eR.tanezumi\u003c/em\u003e density. The proliferation of the \u003cem\u003eR.tanezumi\u003c/em\u003e population has an optimal temperature range (about 20℃). The density of \u003cem\u003eR.tanezumi\u003c/em\u003e was negatively correlated with the current month\u0026apos;s average relative humidity and the precipitation lagged by three months. Additionally, which also exhibited a fluctuating downward trend as the NDVI index lagged by three months increased.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified GAM for Reservoir Impoundment and Drawdown Phases in the TGR area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the influence of hydrology along the Three Gorges Reservoir area on model construction, we analyzed three distinct periods: the impoundment period (from November of the previous year to March of the following year), the drawdown period (from May to September), and the complete annual cycle. We conducted a stratified analysis by partitioning the dataset according to these distinct hydrological periods. Separate (GAM) were developed to quantitatively assess the effects of meteorological variables and vegetation indices on \u003cem\u003eR.tanezumi\u0026nbsp;\u003c/em\u003epopulation density. The model specifications and results are presented in Table 3. Statistical analysis revealed that the impoundment period GAM achieved the highest deviance explained (17.9%), outperforming the drawdown period model in terms of explanatory power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. The formulas and performance of the GAM for different periods in the TGR area\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efrom 2015 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"129%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eModel formulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMonths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eDeviance explained(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eGCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 30px;\"\u003e\n \u003cp\u003eY\u003csub\u003ei,t\u003c/sub\u003e=\u0026alpha;+s\u003csub\u003e1\u003c/sub\u003e(M0Avg_Temp)+s\u003csub\u003e2\u003c/sub\u003e(M0Avg_RHU) +s\u003csub\u003e3\u003c/sub\u003e(M3Precip)+s\u003csub\u003e4\u003c/sub\u003e(M3NDVI)+\u0026epsilon;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eComplete annual cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eJan, Mar, May, Jul, Sep, Nov\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eDrawdown period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMay, Jul, Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eImpoundment period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNov, Jan, Mar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eInteractive effects of ecological drivers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the interactive effects of meteorological variables on \u003cem\u003eR.tanezumi\u003c/em\u003e population dynamics, we incorporated two-way interaction terms between key climatic predictors in GAM.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The interactions between monthly average temperature and relative humidity(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), as well as the cumulative precipitation lagged by 3 months and the NDVI(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), all influence the density of \u003cem\u003eR.tanezumi\u003c/em\u003e. When the temperature is relatively low (5\u0026ndash;10\u0026deg;C), the density of \u003cem\u003eR.tanezumi\u003c/em\u003e is relatively low and shows little change with increasing relative humidity. However, when the temperature rises to above 15\u0026deg;C, the density of \u003cem\u003eR.tanezumi\u003c/em\u003e begins to increase significantly, especially when the relative humidity is between 75% and 85%, where the density reaches a higher level. Furthermore, when the temperature increases further to 20\u0026ndash;25\u0026deg;C, the density of \u003cem\u003eR.tanezumi\u003c/em\u003e decreases under extreme relative humidity conditions (65\u0026ndash;70% or 85\u0026ndash;90%), but remains relatively high under moderate humidity levels (75\u0026ndash;85%). This may suggest that \u003cem\u003eR.tanezumi\u0026nbsp;\u003c/em\u003ehas a certain threshold for environmental humidity, and both excessively high and low humidity levels are unfavorable for its survival (Fig \u0026nbsp;5A).\u003c/p\u003e\n\u003cp\u003eAdditionally, there is a significant interaction between precipitation and the vegetation index. When precipitation is low (50\u0026ndash;100 mm), the density of \u003cem\u003eR.tanezumi\u003c/em\u003e is relatively low when the vegetation index is low (0.3\u0026ndash;0.4), but increases when the vegetation index is high (0.6\u0026ndash;0.7). This indicates that the growth status of vegetation is important for the survival and activity of \u003cem\u003eR.tanezumi\u003c/em\u003e under arid conditions. As precipitation increases (100\u0026ndash;150 mm), the density of \u003cem\u003eR.tanezumi\u003c/em\u003e reaches the highest level (2.0%\u0026ndash;2.5%) in areas with moderate vegetation index (0.4\u0026ndash;0.6). This is likely because moderate precipitation promotes vegetation growth, providing abundant food resources and habitats for \u003cem\u003eR.tanezumi\u003c/em\u003e. However, when precipitation further increases (150\u0026ndash;200 mm), the density of \u003cem\u003eR.tanezumi\u003c/em\u003e decreases in areas with a high vegetation index (0.6\u0026ndash;0.7) (Fig 5B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the context of the World Health Organization's (WHO) global \"One Health\" initiative, which underscores the intricate interplay among humans, animals, and the ecological environment(23, 24), the impact of global changes—such as climate change, land use alterations, and globalization—on the mutation frequency and transmission dynamics of vector-borne infectious diseases has emerged as a critical area of interest. The TGR area is characterized by a subtropical monsoon climate, marked by plentiful rainfall and lush vegetation, including a variety of crops that flourish along its banks(14). This environment is particularly conducive to the proliferation of \u003cem\u003eR.tanezumi\u003c/em\u003e. Our study represents a pioneering effort to explore the impact of environmental determinants, including climatic conditions and landscape features, as well as the interplay among multiple factors, on the observed decline in the population density of these rodents within the region. By conducting a thorough analysis, we aim to offer a scientific foundation for the local management of rodent populations, enhance monitoring efforts, and improve the early detection of diseases transmitted by rats. This research is instrumental in formulating evidence-based strategies for public health and ecological conservation in the area(25).\u003c/p\u003e\n\u003cp\u003eThe investigation found that from 2015 to 2021, the density of \u003cem\u003eR.tanezumi\u003c/em\u003e in the study site identified significant seasonal variations in the population density. The density of these rodents increases during the spring months, particularly in March and May, likely due to the rise in temperature, vegetation growth, and the consequent increase in food resources. This trend continues into the summer months, from June to August, where the density reaches its peak, potentially due to the abundance of food and favorable breeding conditions. Conversely, in the autumn months of September and November, the density shows a decline. These seasonal fluctuations are crucial for understanding the ecological requirements of \u003cem\u003eR.tanezumi\u003c/em\u003e and for forecasting population shifts in response to climate change and alterations in land use patterns. This information is instrumental in devising rodent management strategies that take into account environmental factors. Particularly during months with elevated densities of \u003cem\u003eR.tanezumi\u003c/em\u003e, the implementation of more assertive control measures may be warranted to mitigate potential risks associated with high rodent populations(26).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Italian scholars have revealed the spatial differentiation among the environment, hosts, and vectors by integrating geographic information systems (GIS) and multi-criteria evaluation (MCE) methods, incorporating expert knowledge, mosquito vector distribution data, and host density information. This approach enabled the creation of a vector-borne risk map for Rift Valley Fever (RVF) in Italy(27). Such methodology provides a valuable framework for rodent-borne disease early-warning research. Understanding the distribution dynamics of \u003cem\u003eR.tanezumi\u003c/em\u003e is critical for preventing and controlling the transmission of rodent-borne diseases. Genetic structure diversity studies of \u003cem\u003eR.tanezumi\u003c/em\u003e in China have indicated a dispersal trend of the species from southern China or coastal routes toward the inland Yangtze River Basin(28). Notably, our monitoring results show that the density of \u003cem\u003eR.tanezumi\u003c/em\u003e in the TGR area exhibits a random distribution pattern. However, the driving factors behind the distribution and migration of \u003cem\u003eR.tanezumi\u003c/em\u003e still require further investigation.\u003c/p\u003e\n\u003cp\u003eWe investigated the impact of environmental factors on the population density of \u003cem\u003eR.tanezumi\u003c/em\u003e by considering both immediate and lagged effects(29, 30). Among these factors, monthly average temperature and relative humidity significantly influenced the population density, likely by primarily affecting the activity levels of these rodents. The effect of temperature on the population density of \u003cem\u003eR.tanezumi\u003c/em\u003e exhibited an inverted U-shaped curve, indicating the existence of an optimal range of temperature and humidity for their foraging and activities. This conclusion is corroborated to some extent by studies on the genomic expression and behavioral experiments of \u003cem\u003eR.tanezumi\u003c/em\u003e, which have demonstrated their relatively poor adaptability to cold climates (31, 32). \u0026nbsp; On the other hand, monthly average relative humidity exhibited a negative correlation with the population density of \u003cem\u003eR.tanezumi\u003c/em\u003e. Relative humidity is also a critical factor affecting ectoparasites on rodents. Warm and humid environments facilitate the infection of rodents by ectoparasites such as chigger mites, which may be one of the mechanisms underlying the negative correlation between high humidity and \u003cem\u003eR.tanezumi\u003c/em\u003e density(29, 33, 34). A negative correlation was observed between the population density of \u003cem\u003eR.tanezumi\u003c/em\u003e and both three-month lagged precipitation and three-month lagged NDVI. The potential lag effects of environmental factors on vector organism population density are of critical significance for early warning of rodent infestations and the formulation of proactive management strategies(35). In Indonesia, the population of \u003cem\u003eR.tanezumi\u003c/em\u003e has been found to exhibit a negative response to annual lagged precipitation(36). The negative correlation between NDVI and \u003cem\u003eR.tanezumi\u003c/em\u003e density is more often attributed to urban expansion and land reclamation(8, 37), which reduce vegetation cover indices but provide more suitable living environments for \u003cem\u003eR.tanezumi\u003c/em\u003e\u003cem\u003e，\u003c/em\u003eIt is well known that the \u003cem\u003eR.tanezumiis\u003c/em\u003e a semi-domestic rat that is highly adapted to the human living environment. The increase in the density of \u003cem\u003eR.tanezumiis\u003c/em\u003e in this area may be related to the accelerated urbanization process and the expansion of the human activity area in the local area. These findings from the Generalized Additive Models (GAM) underscore the importance of considering multiple environmental factors when predicting and managing rodent populations, and the identified relationships can inform targeted management strategies(25, 38).\u003c/p\u003e\n\u003cp\u003eWhile this study provides valuable insights, it acknowledges several critical limitations due to data constraints. First, our dataset comprises historical records from 2015 to 2021, which precludes the incorporation of the most recent data. Second, the meteorological raster data employed in our analysis were aggregated at the county level, potentially failing to align precisely with specific monitoring sites. This misalignment may overlook local microclimates, such as urban heat islands and variations in temperature and humidity within vegetated areas(39), thereby introducing measurement bias(40). Additionally, the operational dynamics of the Three Gorges Dam must be considered. The fluctuation in reservoir water levels creates a 30-meter drawdown zone between the elevations of 145 and 175 meters. This zone begins to fill annually from September to October, and the rising water levels may trigger rodent migration, thereby increasing local rodent density(41). Our surveillance indeed detected statistically significant differences in rodent density between the water storage and drainage periods. These complex processes may lead to underestimation or overestimation of the impact of meteorological factors on rodent habitats in our models.\u0026nbsp;Despite the acknowledged limitations, our main findings remain robust.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study's findings robustly affirm the climatic adaptability of \u003cem\u003eR.tanezumi\u003c/em\u003e population density within the Three Gorges Reservoir Area. The research particularly underscores the immediate impacts of temperature and humidity, as well as the delayed influences of precipitation and NDVI on the rodent population(42). These insights are pivotal for shaping policy decisions and enhancing public health preparedness strategies. Our results advocate for heightened vigilance and proactive measures, especially as we approach the month of May—a critical period that may see an increase in the geographical spread of diseases transmitted by \u003cem\u003eR.tanezumi\u003c/em\u003e and other rodents. It is imperative to augment and disseminate active surveillance efforts to track the distribution of these diseases, thereby facilitating a preemptive response to any potential outbreaks(19, 43). By understanding the intricate relationship between climate factors and rodent populations, we can better anticipate and mitigate the risks associated with rodent-borne diseases, safeguarding public health and contributing to effective disease management in the region.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the first comprehensive analysis of the spatiotemporal distribution of \u003cem\u003eR. tanezumi\u003c/em\u003e in the TGR area of Chongqing, China, in relation to climatic and environmental drivers. Our findings identify several key factors shaping its local population density, including the current month's average temperature and relative humidity, as well as three-month-lagged precipitation and NDVI. The period from March to May emerged as a critical window of elevated rodent activity, necessitating targeted surveillance. Against the backdrop of global climate change, this study highlights the urgency of strengthening rodent-borne disease prevention and control strategies in the region. Based on the empirical patterns observed, we recommend that local public health authorities intensify rodent monitoring and control measures each spring (March–May), especially when temperatures reach 15–20°C accompanied by moderate humidity (75–85%), with prioritized efforts in discrete spatial clusters showing significant negative spatial autocorrelation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are grateful to the local county-level CDC stations throughout the Three Gorges Reservoir Area for granting access to the long-term rodent-surveillance database and for their indispensable coordination of field sampling. \u0026nbsp; Specifically, we thank the CDC teams of Fuling, Wanzhou, Kaizhou, Fengdu, Zhong County, Yunyang, Fengjie, and Wushan for their sustained collaboration and logistical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Hansen Xiao, Qiyong Liu and Hengqing Ji conceived and designed the study. Hansen Xiao, Kun Su, Zheng Wang and Taotian Tu performed the experiments and collected the data. Hansen Xiao, Haoqiang Ji, Meng Shang analyzed the data. Hansen Xiao, Qiyong Liu and Hengqing Ji wrote the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was sponsored by the Chongqing Medical Leading Talent Project (No.YXLJ202419); Chongqing Young and Middle-aged Medical High-end Talent Program (No.YXGD202402); Chongqing medical scientific research project(Joint project of Chongqing Health Commission and Science and Technology Bureau) (No.2025MSXM001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmin, R., Ar Salan, M. S., \u0026amp; Hossain, M. M. (2024). Measuring the impact of responsible factors on CO2 emission using generalized additive model (GAM). \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e25416\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e25416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarker, C. M., Chen, Y., Hou, W., \u0026amp; Dong, J. (2023). Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0010806\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0010806\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlasdell, K. R., Morand, S., Laurance, S. G. W., Doggett, S. L., Hahs, A., Trinh, K., Perera, D., \u0026amp; Firth, C. (2022). Rats and the city: Implications of urbanization on zoonotic disease risk in Southeast Asia. \u003cem\u003eProceedings of the National Academy of Sciences, 119\u003c/em\u003e(39). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2112341119\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2112341119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCadavid Restrepo, A. M., Yang, Y. R., McManus, D. P., Gray, D. J., Barnes, T. S., Williams, G. M., Soares Magalh\u0026atilde;es, R. J., Hamm, N. A. S., \u0026amp; Clements, A. C. A. (2018). Spatiotemporal patterns and environmental drivers of human echinococcoses over a twenty-year period in Ningxia Hui Autonomous Region, China. \u003cem\u003eParasites \u0026amp; Vectors\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-018-2693-z\u003c/span\u003e\u003cspan address=\"10.1186/s13071-018-2693-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaminade, C., McIntyre, K. M., \u0026amp; Jones, A. E. (2018). Impact of recent and future climate change on vector-borne diseases. \u003cem\u003eAnnals of the New York Academy of Sciences\u003c/em\u003e, \u003cem\u003e1436\u003c/em\u003e(1), 157\u0026ndash;173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nyas.13950\u003c/span\u003e\u003cspan address=\"10.1111/nyas.13950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, R., Zhang, M., Chen, Y., Hou, G., Liu, Q., Zhang, J., \u0026amp; Zhang, Y. (2024). The special adaptation to hypoxia facilitated the expansion of the Asian house rat (Rattus tanezumi) into Tibet but not other Rattus species. \u003cem\u003eIntegrative Zoology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), 568\u0026ndash;585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1749-4877.12829\u003c/span\u003e\u003cspan address=\"10.1111/1749-4877.12829\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHANG Zhao Rui, LU Liang, MAO De Qiang, PAN Hui Ming, FENG Lian Gui, YANG Xiao Bing, L. F. F., HE Yuan Yuan, ZHANG Jing, \u0026amp; YANG Wei Zhong. (2016). Dynamics of Rodent and Rodent-borne Disease during Construction of the Three Gorges Reservoir from 1997 to 2012*. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3967/bes2016.024\u003c/span\u003e\u003cspan address=\"10.3967/bes2016.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J.-J., Lv, C.-L., Wang, T., Wang, Y.-H., Che, T.-L., Xu, Q., Hong, X.-G., Teng, A.-Y., Tian, S., Zhang, Y.-Y., Liu, M.-C., Wang, L.-P., Hay, S. I., Yang, Y., Fang, L.-Q., \u0026amp; Liu, W. (2025). Small mammals and associated infections in China: a systematic review and spatial modelling analysis. \u003cem\u003eThe Lancet Regional Health - Western Pacific\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lanwpc.2024.101264\u003c/span\u003e\u003cspan address=\"10.1016/j.lanwpc.2024.101264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y.-L., Guo, X.-G., Ding, F., Lv, Y., Yin, P.-W., Song, W.-Y., Zhao, C.-F., Zhang, Z.-W., Fan, R., Peng, P.-Y., Li, B., Chen, T., \u0026amp; Jin, D.-C. (2023). Infestation of Oriental House Rat (Rattus tanezumi) with Chigger Mites Varies along Environmental Gradients across Five Provincial Regions of Southwest China. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph20032203\u003c/span\u003e\u003cspan address=\"10.3390/ijerph20032203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y., Hou, G., Jing, M., Teng, H., Liu, Q., Yang, X., Wang, Y., Qu, J., Shi, C., Lu, L., Zhang, J., \u0026amp; Zhang, Y. (2021). Genomic analysis unveils mechanisms of northward invasion and signatures of plateau adaptation in the Asian house rat. \u003cem\u003eMolecular Ecology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(24), 6596\u0026ndash;6610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.16194\u003c/span\u003e\u003cspan address=\"10.1111/mec.16194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, F. Y., Ge, H. H., Ma, T., Wang, Q., Hao, M. M., Li, H., Zhang, X. A., Maude, R. J., Wang, L. P., Jiang, D., Fang, L. Q., \u0026amp; Liu, W. (2023). Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(23), 6647\u0026ndash;6660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.16969\u003c/span\u003e\u003cspan address=\"10.1111/gcb.16969\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott, I., Thangnimitchok, N., Chaisiri, K., Wangrangsimakul, T., Jaiboon, P., Day, N. P. J., Paris, D. H., Newton, P. N., \u0026amp; Morand, S. (2021). Orientia tsutsugamushi dynamics in vectors and hosts: ecology and risk factors for foci of scrub typhus transmission in northern Thailand. \u003cem\u003eParasites \u0026amp; Vectors\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-021-05042-4\u003c/span\u003e\u003cspan address=\"10.1186/s13071-021-05042-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeit, D. R., Ortiz-Calo, W., Poisson, M. K. P., Butler, A. R., \u0026amp; Moll, R. J. (2024). Generalized nonlinearity in animal ecology: Research, review, and recommendations. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.11387\u003c/span\u003e\u003cspan address=\"10.1002/ece3.11387\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHewitt, J., Zhao, Y., Ge, L., Zhou, Y., Sun, Z., Zheng, E., Wang, X., Huang, Y., \u0026amp; Cheng, H. (2018). A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS). \u003cem\u003ePlos One\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0207518\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0207518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHtwe, N. M., Sudarmaji, Pustika, A. B., Brown, P. R., Stuart, A., Duque, U., Singleton, G. R., \u0026amp; Jacob, J. (2024). Impacts of rainfall and rainfall anomalies on the population dynamics of rodents in southeast Asian rice fields. \u003cem\u003ePest Management Science\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(11), 5574\u0026ndash;5583. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ps.8260\u003c/span\u003e\u003cspan address=\"10.1002/ps.8260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, L.-Q., Guo, X.-G., Speakman, J. R., \u0026amp; Dong, W.-G. (2013). Analysis of gamasid mites (Acari: Mesostigmata) associated with the Asian house rat, Rattus tanezumi (Rodentia: Muridae) in Yunnan Province, Southwest China. \u003cem\u003eParasitology Research\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(5), 1967\u0026ndash;1972. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00436-013-3354-y\u003c/span\u003e\u003cspan address=\"10.1007/s00436-013-3354-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, F., Wang, L., Wang, S., Zhu, L., Dong, L., Zhang, Z., Hao, B., Yang, F., Liu, W., Deng, Y., Zhang, Y., Ma, Y., Pan, B., Han, Y., Ren, H., \u0026amp; Cao, G. (2019). Meteorological factors affect the epidemiology of hemorrhagic fever with renal syndrome via altering the breeding and hantavirus-carrying states of rodents and mites: a 9 years\u0026rsquo; longitudinal study. \u003cem\u003eEmerging Microbes \u0026amp; Infections\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/emi.2017.92\u003c/span\u003e\u003cspan address=\"10.1038/emi.2017.92\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJonathan L. Richardson, Elizabeth P. McCoy, Nicholas Parlavecchio, Ryan Szykowny, Eli Beech- Brown, Jan A. Buijs, Jacqueline Buckley, Robert M. Corrigan, Federico Costa, Ray DeLaney, Rachel Denny, Leah Helms, Wade Lee, Maureen H. Murray, Claudia Riegel, Fabio N. Souza, John Ulrich, Adena Why, \u0026amp; Kiyokawa, Y. (2025). Increasing rat numbers in cities are linked to climate warming, urbanization, and human population.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKe Li, Zhenxu Wang, Xiaohui Wei, Haoqiang Ji, Meng Shang, Nan Chang, Zihao Wang, Chenran Guo, Lei Xu, Ning Zhao, \u0026amp; Liu, Q. (2024). Relationships Between Meteorological Factors and Mongolian Gerbils and Its Flea Burdens \u0026mdash; Xilingol League, Inner Mongolia Autonomous Region, China, 2012\u0026ndash;2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.46234/ccdcw2024.107\u003c/span\u003e\u003cspan address=\"doi: 10.46234/ccdcw2024.107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosoy, M., Khlyap, L., Cosson, J.-F., \u0026amp; Morand, S. (2015). Aboriginal and Invasive Rats of GenusRattusas Hosts of Infectious Agents. \u003cem\u003eVector-Borne and Zoonotic Diseases\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 3\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/vbz.2014.1629\u003c/span\u003e\u003cspan address=\"10.1089/vbz.2014.1629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKouroupis, D., Charisi, K., \u0026amp; Pyrpasopoulou, A. (2023). The Ongoing Epidemic of West Nile Virus in Greece: The Contribution of Biological Vectors and Reservoirs and the Importance of Climate and Socioeconomic Factors Revisited. \u003cem\u003eTropical Medicine and Infectious Disease\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/tropicalmed8090453\u003c/span\u003e\u003cspan address=\"10.3390/tropicalmed8090453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreppel, K. S., Telfer, S., Rajerison, M., Morse, A., \u0026amp; Baylis, M. (2016). Effect of temperature and relative humidity on the development times and survival of Synopsyllus fonquerniei and Xenopsylla cheopis, the flea vectors of plague in Madagascar. \u003cem\u003eParasites \u0026amp; Vectors\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-016-1366-z\u003c/span\u003e\u003cspan address=\"10.1186/s13071-016-1366-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulkarni, M. A., Berrang-Ford, L., Buck, P. A., Drebot, M. A., Lindsay, L. R., \u0026amp; Ogden, N. H. (2019). Major emerging vector-borne zoonotic diseases of public health importance in Canada. \u003cem\u003eEmerging Microbes \u0026amp; Infections\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/emi.2015.33\u003c/span\u003e\u003cspan address=\"10.1038/emi.2015.33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Huang, E., Wu, Y., Zhu, C., Li, W., Ai, L., Xie, Q., Tian, Z., Zhong, W., Sun, G., Zhang, L., \u0026amp; Tan, W. (2024). Population structure, dispersion patterns and genetic diversity of two major invasive and commensal zoonotic disease hosts (Rattus norvegicus and Rattus tanezumi) from the southeastern coast of China. \u003cem\u003eFrontiers in Genetics, 14\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2023.1174584\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2023.1174584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, K., Chen, Y., \u0026amp; Jiang, J. (2023). Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envint.2023.108196\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2023.108196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, K., Sommer, S., Yang, Z., Guo, Y., Yue, Y., Ozgul, A., \u0026amp; Wang, D. (2022). Distinct body-size responses to warming climate in three rodent species. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences, 289\u003c/em\u003e(1972). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2022.0015\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2022.0015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, R.-J., Guo, X.-G., Peng, P.-Y., Lv, Y., Yin, P.-W., Song, W.-Y., Xiang, R., Chen, Y.-L., Li, B., \u0026amp; Jin, D.-C. (2025). Mite Infestation on Rattus tanezum rats in southwest China concerning risk models. \u003cem\u003eFrontiers in Veterinary Science\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fvets.2025.1519188\u003c/span\u003e\u003cspan address=\"10.3389/fvets.2025.1519188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, R.-J., Guo, X.-G., Zhao, C.-F., Zhao, Y.-F., Peng, P.-Y., \u0026amp; Jin, D.-C. (2024). An Ecological Survey of Chiggers (Acariformes: Trombiculidae) Associated with Small Mammals in an Epidemic Focus of Scrub Typhus on the China\u0026ndash;Myanmar Border in Southwest China. \u003cem\u003eInsects\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/insects15100812\u003c/span\u003e\u003cspan address=\"10.3390/insects15100812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoan, H. K., Van Cuong, N., Takhampunya, R., Kiet, B. T., Campbell, J., Them, L. N., Bryant, J. E., Tippayachai, B., Van Hoang, N., Morand, S., Hien, V. B., \u0026amp; Carrique-Mas, J. J. (2015). How Important Are Rats As Vectors of Leptospirosis in the Mekong Delta of Vietnam? \u003cem\u003eVector-Borne and Zoonotic Diseases\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 56\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/vbz.2014.1613\u003c/span\u003e\u003cspan address=\"10.1089/vbz.2014.1613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadelynn H. Howard, Christie M. Sayes, John P. Giesy, \u0026amp; a, Y. L. (2024). Valley fever under a changing climate in the United States. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envint.2024.109066\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2024.109066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunoz-Zanzi, C., Talmoudi, K., Bellali, H., Ben-Alaya, N., Saez, M., Malouche, D., \u0026amp; Chahed, M. K. (2017). Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009\u0026ndash;2015: Forecasting models using climate variables as predictors. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pntd.0005844\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0005844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavindra, K., Rattan, P., Mor, S., \u0026amp; Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envint.2019.104987\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2019.104987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandy, J., Uea-Anuwong, T., Kiu, L. H., Lee, L. K. F., Abdullah, S., \u0026amp; Magouras, I. (2024). Capillaria hepatica (syn. Calodium hepaticum) infection and factors influencing infection carriage in rats (Rattus spp.) in Hong Kong. \u003cem\u003eOne Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.onehlt.2024.100878\u003c/span\u003e\u003cspan address=\"10.1016/j.onehlt.2024.100878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSehgal, A., Mehta, S., Sahay, K., Martynova, E., Rizvanov, A., Baranwal, M., Chandy, S., Khaiboullina, S., Kabwe, E., \u0026amp; Davidyuk, Y. (2023). Hemorrhagic Fever with Renal Syndrome in Asia: History, Pathogenesis, Diagnosis, Treatment, and Prevention. \u003cem\u003eViruses\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/v15020561\u003c/span\u003e\u003cspan address=\"10.3390/v15020561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaheen, M. N. F. (2022). The concept of one health applied to the problem of zoonotic diseases. \u003cem\u003eReviews in Medical Virology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/rmv.2326\u003c/span\u003e\u003cspan address=\"10.1002/rmv.2326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwamy, G. S. N. V. K. S. N., Nagendra, S. M., \u0026amp; Schlink, U. (2020). Impact of urban heat island on meteorology and air quality at microenvironments. \u003cem\u003eJournal of the Air \u0026amp; Waste Management Association\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(9), 876\u0026ndash;891. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10962247.2020.1783390\u003c/span\u003e\u003cspan address=\"10.1080/10962247.2020.1783390\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomson, M. C., Solomon, C. G., Salas, R. N., \u0026amp; Stanberry, L. R. (2022). Climate Change and Vectorborne Diseases. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e, \u003cem\u003e387\u003c/em\u003e(21), 1969\u0026ndash;1978. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMra2200092\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra2200092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaojun Wua, Lunche Wang, Qian Cao, Zigeng Niu, \u0026amp; Xin Dai. (2023). Regional climate change and possible causes over the Three Gorges Reservoir Area. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2023.166263\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2023.166263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, M.-Y., Cao, R.-D., Chen, Y., Ma, J.-C., Shi, C.-M., Zhang, Y.-F., Zhang, J.-X., Zhang, Y.-H., \u0026amp; Su, B. (2024). Genomic and Phenotypic Adaptations ofRattus tanezumito Cold Limit Its Further Northward Expansion and Range Overlap withR. norvegicus. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/molbev/msae106\u003c/span\u003e\u003cspan address=\"10.1093/molbev/msae106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., Peng, L., Liu, W., Fan, L., Zhang, Q., Chen, G., He, P., Wu, R., Liu, A., Yang, Y., Yu, X., \u0026amp; Yun, L. (2014). Response of primary vectors and related diseases to impoundment by the Three Gorges Dam. \u003cem\u003eTropical Medicine \u0026amp; International Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(4), 440\u0026ndash;449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/tmi.12272\u003c/span\u003e\u003cspan address=\"10.1111/tmi.12272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, W., Zhou, H., Yang, L., Ma, T., Zhou, J., Liu, H., Lu, G., \u0026amp; Huang, H. (2020). Prevalence, genetic diversity and implications for public health of Enterocytozoon bieneusi in various rodents from Hainan Province, China. \u003cem\u003eParasites \u0026amp; Vectors\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-020-04314-9\u003c/span\u003e\u003cspan address=\"10.1186/s13071-020-04314-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinsstag, J., Crump, L., Schelling, E., Hattendorf, J., Maidane, Y. O., Ali, K. O., Muhummed, A., Umer, A. A., Aliyi, F., Nooh, F., Abdikadir, M. I., Ali, S. M., Hartinger, S., M\u0026auml;usezahl, D., de White, M. B. G., Cordon-Rosales, C., Castillo, D. A., McCracken, J., Abakar, F., Cercamondi, C., Emmenegger, S., Maier, E., Karanja, S., Bolon, I., de Casta\u0026ntilde;eda, R. R., Bonfoh, B., Tschopp, R., Probst-Hensch, N., \u0026amp; Ciss\u0026eacute;, G. (2018). Climate change and One Health. \u003cem\u003eFEMS Microbiology Letters\u003c/em\u003e, \u003cem\u003e365\u003c/em\u003e(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsle/fny085\u003c/span\u003e\u003cspan address=\"10.1093/femsle/fny085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo, L., Wang, H., Tan, Y., Wan, J., Tan, W., Gan, Y., Xiong, X., Wang, J., \u0026amp; Luo, C. (2024). Co-circulation of Hantavirus, Pathogenic Leptospira spp., and Bartonella spp. in Rodents in the Wanzhou Section of the Three Gorges Reservoir Region, 2021\u0026ndash;2023. \u003cem\u003eVector-Borne and Zoonotic Diseases\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(10), 694\u0026ndash;698. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/vbz.2023.0150\u003c/span\u003e\u003cspan address=\"10.1089/vbz.2023.0150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rattus tanezumi, Three Gorges Reservoir, spatiotemporal dynamics, environmental drivers, GAM, Disease ecology","lastPublishedDoi":"10.21203/rs.3.rs-7896124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7896124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eRattus tanezumi\u003c/em\u003e(\u003cem\u003eR. tanezumi\u003c/em\u003e), a major vector for plague, leptospirosis, and hantavirus, exhibits population dynamics in the Three Gorges Reservoir (TGR) Area of China that are influenced by climate and reservoir-induced environmental changes, thereby impacting disease transmission risks. This study investigated the spatiotemporal dynamics of \u003cem\u003eR. tanezumi\u003c/em\u003e and its association with key meteorological and environmental factors from 2015 to 2021. Using Geographic Information Systems (GIS) for spatiotemporal analysis and a Generalized Additive Model (GAM), we identified the current month's average temperature and relative humidity, together with three-month-lagged precipitation and the Normalized Difference Vegetation Index (NDVI), as significant drivers. The relationship was modeled (Yi,t\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;s\u003csub\u003e1\u003c/sub\u003e(M0Avg_Temp)\u0026thinsp;+\u0026thinsp;s\u003csub\u003e2\u003c/sub\u003e(M0Avg_RHU)\u0026thinsp;+\u0026thinsp;s\u003csub\u003e3\u003c/sub\u003e(M3Precip)\u0026thinsp;+\u0026thinsp;s\u003csub\u003e4\u003c/sub\u003e(M3NDVI) + ε; R\u0026sup2;= 0.12, Deviance explained\u0026thinsp;=\u0026thinsp;14%, GCV\u0026thinsp;=\u0026thinsp;0.4438), revealing an optimal proliferation temperature around 20\u0026deg;C. Density showed negative correlations with both current relative humidity and lagged precipitation, and a fluctuating decline with increasing lagged NDVI. As the first investigation of its kind, this study demonstrates how climatic and environmental factors shape the spatiotemporal distribution of \u003cem\u003eR. tanezumi\u003c/em\u003e in the TGR Area, providing critical insights for predicting outbreaks and formulating targeted surveillance and control strategies against rodent-borne diseases in this vulnerable region.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal dynamics of Rattus tanezumi density and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 15:12:52","doi":"10.21203/rs.3.rs-7896124/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-05T01:24:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T13:10:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T13:08:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-10-19T03:04:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"293ed552-07b3-40ad-85da-1754675c9ae8","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T14:10:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 15:12:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7896124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7896124","identity":"rs-7896124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}