Genetic dynamics and dispersion pattern changes of Aedes aegypti populations in Yunnan Province from 2016 to 2018

Genetic dynamics and dispersion pattern changes of Aedes aegypti populations in Yunnan Province from 2016 to 2018 | 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 Genetic dynamics and dispersion pattern changes of Aedes aegypti populations in Yunnan Province from 2016 to 2018 Jian Gao, Heng-Duan Zhang, Zhi-Ming Wu, Dan Xing, Xiao-Xia Guo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8827646/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Aedes aegypti is the major vector for massive mosquito-borne viruses, including Dengue, ZIKV, etc., and threatens human health world-widely. The absence of efficient vaccine for those insect-borne diseases highlights the importance of monitoring of genetic changes of the Ae. aegypti populations. Methods The genetic variation and population structure of Ae. aegypti populations collected from three regions of Yunan province in China during 2016 to 2018 were investigated with 9 microsatellite loci and the mitochondrial coxI gene. Results From 2016 to 2018, the genetic diversity of Ae. aegypti populations in Yunnan province displayed a trend of initial increase followed by a decrease. The degree of inbreeding within the populations gradually shifted from heterozygous to moderately inbred. The clustering results indicated that, compared to the populations collected in 2016, those collected in 2017 and 2018 were genetically closer. Ten coxI haplotypes were detected, haplotypes H06 and H09 were detected nearly in all regions, while the others were only detected in single region or even single year. The population diffusion analysis indicated that the diffusion of Ae. aegypti populations among different locations exhibited a trend of first increasing and then decreasing. Meanwhile, the AMOVA analysis results also revealed that the source of variation in the Yunnan Ae. aegypti populations gradually shifted from mainly inter-individual to mainly inter-regional. The evolutionary scenario inference of the invasion and diffusion of Ae. aegypti indicated that the mosquito invaded and colonized Dehong Prefecture from Southeast Asian, and then diffused to Lincang City, and finally to Xishuangbanna Prefecture. Conclusion The results presented here indicate that the current mosquito-control strategies in Yunnan Province have effectively suppressed Ae. aegypti populations within regions. Rather than focusing primarily on preventing imported cases, greater attention should now be paid to intra-provincial spread of Ae. aegypti , particularly between port and urban areas. Aedes aegypti genetic structure invasion dispersion and Microsatellite Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Mosquito-borne viruses, particularly dengue virus (DENV), Zika virus (ZIKV), and Chikungunya virus (CHIKV), pose a serious threat to public health globally [ 1 ] . As reported, over 40% of the world’s population were affected by DENV alone and infect millions of people worldwide every year [ 2 ] . To date, there is no efficient preventive vaccine available for these infectious diseases [ 3 , 4 ] . Thus, continuous surveillance activities and tailored control interventions are desperately needed to protect human health, especially for those living in tropical and sub-tropical regions, where continuous mosquito breeding is possible. Aedes aegypti (Linnaeus, 1762), an invasive species in China, is responsible for the transmission of dengue virus. Compared to Ae . albopictus (Skuse, 1894), Ae. aegypti is largely endophilic, survive in water-filled spaces, and prefer day-biting [ 5 , 6 ] . Outbreaks of related arboviruses can occur even at low Ae. aegypti densities, especially as mosquitoes breed near human dwellings [ 4 ] . Meanwhile, as an invasive species, it also possesses greater ecological niche plasticity, which potentially assists the mosquito in adapting to new environments or even replacing the native mosquito species as a dominant species, posing a greater threat to public health and safety [ 7 , 8 ] . As reported, Ae. aegypti is strongly associated with urban environments, and its distribution seems to be affected by climate and socioeconomic factors [ 9 , 10 ] . Since the advancement of urbanization in China and the influence of factors such as global climate change, the distribution of Ae. aegypti in China is exhibiting a trend of expansion from south to north [ 11 ] . Therefore, real-time monitoring of the invasion and diffusion trend of Ae. aegypti in China is of great significance for the prevention and control of related arboviruses. Yunnan province, situated in the southwestern region of China, borders various Southeast Asian nations impacted by DENV, ZIKV and CHIKV, such as Laos, Vietnam, and Myanmar. Continuous tourism, trade, and labor export between Yunnan and these countries has led to a steady increase in imported cases of Arboviruses infection [ 35 , 36 ] . Meanwhile, the geographical and climatic conditions of Yunnan province also favor the breeding of major mosquito vector of dengue virus, Ae. aegypti , which may enhance the severity of dengue fever epidemic in local and even the whole country. Therefore, monitoring the population dynamics of Ae. aegypti in this area is also crucial for the prevention and control of dengue fever epidemics. The assessment of mosquito biodiversity, including genetic structuring and population dispersion, is the crucial step for developing and implementing appropriate strategies to control mosquito vector populations and subsequently the mosquito-borne infectious diseases [ 12 ] . Of all the molecular markers, Microsatellite loci and mitochondrial gene Cox I are the most common markers for its codominance, polymorphism, and moderate evolutionary rate, respectively. i.e. , Luke et al. (2014) used these markers to assess the population genetic structure of Anopheles farauti and found strong gene flow barriers among An. farauti populations in the Solomon Archipelago [ 13 ] . Singh et al. (2025) used these makers to distinguish the genetic diversity and coancestry of the captive individuals [ 14 ] . Noorullah et al. (2025) used these makers to assess the genetic diversity among wild and captive-bred Labeo rohita [ 15 ] . To date, numerous investigations have focused on the population structure of Ae. aegypti [ 16 – 20 ] . However, limited research has compared and analyzed the dynamics and dispersal patterns of mosquito populations across successive years. Thus, this study monitored the Ae. aegypti population in various regions of Yunnan Province in 2017 and 2018. By integrating the population data from 2016 [ 16 ] , the study elucidated the population dynamics of Ae. aegypti in Yunnan Province over a three-year period. Utilizing 9 microsatellite loci and mitochondrial coxI gene, the study assessed the impact of current dengue management strategies on the Ae. aegypti population. The findings aim to offer valuable insights into future dengue control initiatives. Material methods Mosquito sampling and processing Ae. aegypti mosquitoes were collected from three different regions across Yunnan province, from 2017 to 2018. The sample information and characteristics of each sample site were given in Table 1 . The Ae. aegypti investigation was mainly conducted in indoor areas of each residential area with a focus on investigating the breeding grounds of larvae. All larvae were transferred to the lab with isolated containers and maintained at a temperature of 28℃ and 75% relative humidity with a 14:10-h (Light: Dark) photoperiod until emergency. The emerging adults were then morphologically identified under a dissecting microscope using taxonomic keys of Lu and Norbert [ 21 , 22 ] , the identified female Ae. aegypti were then stored in 95% ethanol − 80°C until usage. Table 1 The sample information and characteristics of each Ae. aegypti sample site among different regions of Yunnan province, China Regions Sample site Geographical Coordinates Sample size Year 2016 Year 2017 Year 2018 Banna prefecture Jinghong city 100°47′58.279″E,22°0′42.894″N 120 90 120 Menla city 101°41′7.843″E,21°11′35.225″N 59 61 60 Daluo port 100°1′58.310″E,21°40′46.330″N 50 91 90 Dehong prefecture Dehong 97°51′19.667″E,24°1′4.375″N 150 90 163 Lincang Lincang city 99°24′10.184″E,23°32′19.781″N 60 92 50 DNA isolation , coxI gene amplification, and microsatellite genotyping At least 30 samples per site were randomly selected for the following molecular analysis. Referring to the manufacturer’s protocol, all samples were milled with a freeze grinder, and the nucleic acid was extracted using a fully automatic nucleic acid extractor with the DNeasy Blood & Tissue Kit (Qiagen, USA). All DNA samples were stored at -80℃. Referring to Gao et al. [ 23 ] , a 550bp fragment of coxI gene was amplified with the primers: 5′-GGAGGATTTGGAAATTGATTAGTTC-3′ ( CoxI -Forward) and 5′-CCCGGTAAAATTAAAATATAAACTTC-3′ ( CoxI -Reverse). Three µL of mosquito DNA was amplified with a TAdvanced Thermal Cycler (Analytikjena, Germany) in a 50 µL reaction system, containing 5 µL PCR buffer (TaKaRa, Japan), 4 µL dNTPs (2.5 mM each, TaKaRa, Japan), 1 µL primers (10 pmol/µL, TaKaRa, Japan), 0.5 µL PrimeSTAR HS DNA polymerase (TaKaRa, Japan) and 15.5 µL ddH 2 0.The application program is set as pre-denaturation at 95°C for 3 min, followed by 35 cycles of denaturation at 95°C for 45 s, annealing at 55°C for 30 s, and elongation at 72°C for 1 min, with a final elongation at 72°C for 7 min. All PCR products were detected by 1.2% agarose gel electrophoresis and sequenced on an ABI 3730XL automatic sequencer (Applied Biosystems, United States). Referring to Shi et al. [ 16 ] , nine microsatellite loci were selected for microsatellite genotyping. The forward primer sequence of each locus was end-labeled with different fluorescent dyes, FAM, HEX, or TAMRA. All the primers were amplified at the optimal temperature with a TAdvanced Thermal Cycler (Analytikjena, Germany), and the products were analyzed with ABI 3730XL automatic sequencer (Applied Biosystems, United States). The data was analyzed with the software Gene Marker (Version 2.2.0). Genetic diversity, differentiation and population clustering Genetic diversity indexes, such as the mean number of alleles per locus ( Na ), observed heterozygosity ( Ho ), expected heterozygosity ( He ), and positive inbreeding coefficient (F IS ), were calculated with the software popgen32 (version 1.32) Based on the mitochondrial coxI gene, the haplotype were calculated with the software Dna SP (version 6.12.03) [ 24 ] . DAPC analysis of population structure of Ae. aegypti was performed by DAPC function in R software package "Adegenet 2.1.3" [ 25 ] . According to the nucleotide sequence characteristics of each haplotype, genetic evolution analysis and regional distribution analysis of each haplotype were carried out by BEAST v.1.8.4 [ 26 ] , among which the best model of BEAST was determined by JModelTest v.2.1.10calculation [ 27 ] . AMOVA test and Haplotype diversity analysis Genetic diversity and molecular variance of all Ae. aegypt i populations assessed using Analyses of Molecular Variance (AMOVA) in Arlequin v3.5 [ 28 ] . Meanwhile, the nucleotide mutation network between haplotypes (TCS network) was calculated by DnaSP v.6.0 and Arlequin v.3.5.2.2 software and constructed by Network v.10.0.0.0 software [ 29 , 30 ] . Population similarity, invasion and dispersion analysis Based on the gene flow data of Ae. aegypti , the dispersion routine between 5 regions was evaluated by using the divMigrate function in R software package "diversity v.1.9.90" [ 31 ] , where the guided replication value was set to 3 and 5, the alpha value was set to 0.05, and the gene flow method was selected for diffusion network construction, and the threshold value was set to 0.25 and 0.35, respectively. And the population similarity was displayed via R package "Heatmap v.1.1.9" [ 32 ] . To infer the invasion and dispersion history of Ae. aegypti in Yunnan province, the Bayesian computation methods were performed on the microsatellite dataset via DIYABC v.2.0.4 [ 33 ] . According to the manual of the software and the dispersion analysis results of Ae. aegypti , four scenarios were tested. Scenarios 1 and 3 propose that, Ae. aegypti invaded Dehong Prefecture and Xishuangbanna Prefecture simultaneously from abroad area, while scenarios 2 and 4 assume that, Ae. aegypti first invaded Dehong Prefecture from abroad area. For analysis, 1,000,000 data sets for each scenario were first simulated, resulting in 4,000,000 total simulated datasets. The relative posterior probability (PP) of each scenario was then evaluated using logistic regression on the 4,000 simulated datasets closest to the observed dataset to determine which scenario was most supported by the data. Results Genetic diversity variation of Ae. aegypti populations during 2016 to 2018 Consisting of microsatellite datasets, the ne value for Ae. aegypti population of each sample site ranged from 2.16 to 3.33 during 2016 to 2018. The population heterozygosity analysis results revealed that in 2016, the observed heterozygosity ( Ho ) of Ae. aegypti population within Lincang City, Daluo port, and Menla City ( Ho , 0.61 ~ 0.65) exceeded the expected value ( He , 0.58 ~ 0.61). In 2017 and 2018, the Ho value of each Ae. aegypti populations was lower than expected ( He ). Our analysis also revealed that the inbreeding coefficient values (F IS ) of Ae. aegypti populations from Mengla City, Daluo Port, and Lincang City in 2016 were negative (ranged from − 0.07 to -0.04), whereas all other Ae. aegypti populations displayed positive inbreeding coefficients (Table 2 ). Moreover, the F IS values for each population showed a trend of first increasing and then decreasing over the years. Table 2 Genetic diversity indices of Ae. aegypti populations sampled from five major regions of Yunnan province during 2016 to 2018 based on 9 microsatellite loci Sample site 2016 2017 2018 ne Ho He F IS ne Ho He F IS ne Ho He F IS Jinghong 3.30 0.62 0.65 0.05 3.33 0.40 0.61 0.34 2.82 0.56 0.59 0.07 Dehong 3.18 0.55 0.64 0.14 3.26 0.41 0.62 0.27 2.87 0.45 0.57 0.18 Menla 2.96 0.65 0.61 -0.07 2.65 0.43 0.55 0.23 2.24 0.47 0.53 0.12 Daluo 2.93 0.62 0.59 -0.04 2.82 0.43 0.57 0.25 2.75 0.53 0.58 0.09 Lincang 2.78 0.61 0.58 -0.06 3.33 0.44 0.61 0.29 2.16 0.41 0.50 0.23 *ne: Effective number of alleles [Kimura and Crow (1964)]; Ho : Observed heterozygosity; He : expected heterozygosity; F IS : inbreeding coefficient Genetic structure within five major regions of Yunnan province Population structure analysis revealed significant changes in Ae. aegypti population structure across five sampling sites from 2016 to 2018. Initially, in 2016, two distinct branches (K = 2) were observed in the UGPMA tree. The Ae. aegypti populations from Jinghong City and Dehong clustered together, whereas those from Lincang City, Daluo Port, and Mengla City formed the other branch (Fig. 1 A and D). In 2017, the mosquito populations were genetically divided into two branches again, with Lincang City forming a separate branch from the other four regions, where the mosquito populations from Jinghong City and Daluo Port exhibited greater compositional similarity compared to those from Mengla City and Dehong (Fig. 1 B and E). In 2018, although the mosquito populations were once more divided into two branches, populations from Lincang City and Dehong clustered into one branch, and Mengla City, Daluo Port, and Jinghong City formed the other one. Notably, Ae. aegypti populations from Jinghong City and Daluo Port displayed greater similarity in composition (Fig. 1 C and F). Analysis of molecular variance (AMOVA) test of all Ae. aegypti populations The AMOVA analysis of all Ae. aegypti populations revealed that the primary sources of variation among the Ae. aegypti populations of different regions from 2016 to 2018 were predominantly attributed to individuals, with proportions of 80.16%, 90.38%, and 91.62%, respectively. The molecular variances within Ae. aegypti populations in 2017 and 2018 exhibited considerable similarity, with a notable contrast found in 2018, where the proportion of variance within individuals was notably higher at 80.36% compared to 65.15% in 2017. In contrast, in 2016, 11.26% of the molecular variations in Ae. aegypti populations were attributed to inter-regional differences, whereas 8.58% of the molecular variations stemmed from populations within regions (Table 3 ). Table 3 Analysis of molecular variance (AMOVA) test of all Ae. aegypti populations sampled from Yunnan province from 2016 to 2018 Source of Variation 2016 2017 2018 Variance Components Percentage of Variation Variance Components Percentage of Variation Variance Components Percentage of Variation Among regions 0.397Va 11.26 0.161Va * 5.51 0.120Va ** 5.02 Among populations within regions 0.302Vb 8.58 0.120Vb * 4.11 0.080Vb ** 3.36 Among individuals Within populations 0.132Vc 3.73 0.739Vc * 25.23 0.270Vc ** 11.26 Within individuals 2.693Vd 76.43 1.908Vd * 65.15 1.924Vd ** 80.36 Haplotype diversity and composition dynamics of all Ae. aegypti populations among different regions Based on the mitochondrial c oxI gene, a total of 10 haplotypes were identified from all Ae. aegypti populations, named haplotype H07, H12, H05, H13, H14, H06, H04, H29, H09, and H30. Of all these haplotypes, H06 and H09 were dominant (Fig. 2 c). Molecular cluster analysis of haplotype sequences showed that haplotypes H07, H12, H05, H13, H14, and H06 exhibited only 1 ~ 2 gene mutations with the standard Ae. aegypti CoxI gene sequnce, whereas haplotypes H04, H29, H09, and H30 displayed multiple gene mutations (Fig. 2 a). Haplotype diversity analysis across different years revealed that the haplotype diversity of Ae. aegypti populations in Jinhong City, Dehong, Mengla City, and Lincang City gradually decreased over the years, whereas those at Daluo Port remained relatively stable (Fig. 2 b). Population dispersion and population structure similarity analysis of Ae. aegypti populations Two major dispersal cycles were discovered among different regions in 2016, i.e. ,1) interdiffusion between Jinghong City and Dehong (NM = 0.49 and 0.55, respectively), and 2) circular dispersal from Mengla City to Daluo Port to Lincang City (NM = 0.56, 0.55 and 0.57, respectively), with notable interdiffusion between Mengla City and Daluo Port (NM = 0.56 and 1, respectively) (Fig. 3 a). In 2017, Ae. aegypti populations displayed interdiffusion among various regions, with gene flow exceeding 0.5. Strong interdiffusion was observed between Jinghong City and Dehong (NM = 0.60 and 0.56, respectively), Dehong and Lincang City (NM = 0.68 and 0.58, respectively), Jinghong City and Daluo Port (NM = 0.97 and 1, respectively), Daluo Port and Menla City (NM = 1 and 0.56, respectively), and Dehong and Daluo Port (NM = 0.50 and 0.58, respectively). Additionally, significant diffusion trends were noted from Mengla City to Dehong (NM = 0.83) and from Jinghong City to Mengla City (NM = 0.54) (Fig. 3 b). By 2018, robust population diffusion was only evident between Daluo Port and Jinhong City (NM = 0.97 and 1, respectively) and from Mengla City to Jinghong City (NM = 0.51) (Fig. 3 c). The heat map analysis results based on the population diversity index revealed notable population similarities among Ae. aegypti populations in different regions. In 2016, high similarities were observed between the Daluo Port and Mengla city populations, Mengla city and Lincang city populations, Daluo Port and Lincang city populations, and between the Dehong and Jinghong city populations, with similarity indices of 0.83, 0.67, 0.52, and 0.64, respectively (Fig. 3 d). In 2017, high similarities were found between the Jinhong city and Daluo Port populations, the Dehong and Mengla City populations, and the Dehong and Lincang city populations, with similarity indices of 0.84, 0.27, and 0.32, respectively (Fig. 3 e). Similarly, in 2018, a high population similarity was observed between the Jinghong city and Daluo Port populations, Jinghong city and Mengla City populations, and Mengla City and Daluo Port populations, with similar indices of 0.90, 0.70, and 0.42, respectively (Fig. 3 f). Inferring invasion and dispersion history of Ae. aegypti in Yunnan province According to Fig. 4 , scenario 1 suggests that, Ae. aegypti population in Lincang City originated from the spread of mosquitoes in Xishuangbanna Prefecture (PP = 0.292), whereas scenario 3 posited that the population in Lincang City stemmed from the spread in Dehong Prefecture (PP = 0.054). Conversely, Scenarios 2 and 4 assumed that Ae. aegypti first invaded Dehong Prefecture from abroad. In this context, Scenario 2 proposes that Ae. aegypti initially spread to Lincang City before moving to Xishuangbanna Prefecture (PP = 0.509). scenario 4, on the other hand, suggests that, Ae. aegypti first spread to Lincang City and subsequently migrated to Xishuangbanna Prefecture (PP = 0.145). Overall, the simulation calculation results of the software were more supportive of the authenticity of Scenario 2. Discussion Genetic diversity is an important genetic index to evaluate population adaptability, which can be estimated by the observed number of alleles, observed heterozygosity, and expected heterozygosity [ 34 ] . Many studies have shown that unintentional parental selection and inbreeding in the process of reproduction can lead to a decrease in the genetic diversity of populations [ 26 – 32 ] . In present study, the observed heterozygosity ( Ho ) values for Ae. aegypti populations in the Jinghong City and Mengla City were consistently lower than the expected heterozygosity ( He ) values across different years, while the inbreeding coefficient (F IS ) values were all greater than 0. This pattern suggests that the Ae. aegypti populations in these regions have been in a prolonged state of inbreeding. Furthermore, the analysis of effective alleles indicated that the number of effective alleles in both populations initially increased before subsequently declining. Concurrently, haplotype analysis revealed a year-on-year decrease in haplotype diversity. As for Mengla City, Daluo Port, and Lincang City, the population heterozygosity over the monitoring years transitioned from Ho > He to Ho < He , with F IS shifting from negative to positive. These findings indicate that the Ae. aegypti population in Yunnan is experiencing ongoing inbreeding, which consequently leads to the decreasing in the genetic diversity of the entire population. Haplotype structure is also important in understanding the evolution of a species and the dispersion of populations within it, as the rare haplotypes could also reflect a spreading population [ 44 – 46 ] . In this study, only haplotype H06 was consistently detected in Ae. aegypti populations from all regions across years, while H09 was detected in Ae. aegypti populations except in Lincang City. Other haplotypes were only detected in single regions or even single years. Specifically, haplotype H07 was detected in Ae. aegypti populations from Banna Prefecture across all years and was also detected in the Daluo Port population in 2018. Combined with the analysis results of the diffusion of Ae. aegypti populations across different regions in 2018, this also verifies the stable diffusion between the Ae. aegypti populations in Banna Prefecture and those at the Daluo Port. Haplotypes H14, H29, and H30 were only detected in Dehong Prefecture in 2016 and 2017, respectively, which may indicate newly invaded Ae. aegypti populations that have not successfully colonized. Therefore, in subsequent monitoring, we can focus on using these specific Ae. aegypti haplotypes to conduct real-time monitoring of the diffusion of Ae. aegypti populations between different locations. Apart from the environmental factors, such as landscape change, temperature, rainfall, etc. , local mosquito control activities are also major reasons that influence the mosquito population structure significantly [ 47 ] . In present study, the clustering results of Ae. aegypti populations reveal that, compared to those collected in 2016, the populations collected in 2017 and 2018 are genetically closer. This similarity may be attributed to the local continuous prevention and control strategies against dengue-transmitting Aedes mosquitoes, by which, the local Ae. aegypti populations have been significantly changed. Meanwhile, the continuous disinfection and sterilization of vehicles and goods entering the border have also reduced the cross-border spread of Ae. aegypti . The genetic clustering for Ae. aegypti populations closely correspond with the findings of population diversity, which showed that the Ae. aegypti population in Yunnan in 2016 had a lower inbreeding coefficient due to extensive gene exchange with exogenous Ae. aegypti populations, resulting in a heterozygous state. However, the Ae. aegypti populations in 2017 and 2018 displayed moderate inbreeding state. This study discovered that from 2016 to 2018, the diffusion of Ae. aegypti populations among different regions in Yunnan exhibited a trend of initial increase followed by a decrease, which may be attributed to the implementation of local dengue fever prevention and control strategies. Following the local dengue epidemic caused by imported cases in Yunnan in 2015, Yunnan gradually implemented control measures against Ae. aegypti . The population inbreeding coefficient and population diffusion map from 2016 to 2017 showed that although the invasion of Ae. aegypti from outside the country into the interior was reduced, the mutual diffusion of Ae. aegypti among different regions in Yunnan was still not effectively controlled. Therefore, by 2017, the Ae. aegypti populations among the five regions in Yunnan were basically in a state of mutual diffusion. In 2017, the government introduced a comprehensive local prevention and control plan for Ae. aegypti (the "Xishuangbanna Dengue Fever Prevention and Control Propaganda Work Plan (2017)"), which effectively controlled the local diffusion of Ae. aegypti populations. Consequently, by 2018, the Ae. aegypti populations were only diffusing between Banna and Dehong prefecutres. Meanwhile, the AMOVA analysis is based on the mitochondrial gene CoxI of Ae. aegypti also confirmed this hypothesis, indicating that the variation in Ae. aegypti populations across Yunnan in 2016 and 2017 mainly originated from within the Ae. aegypti populations themselves, accounting for 79.48% and 74.18% of the total variation, respectively. However, by 2018, the variation in Ae. aegypti populations in Yunnan mainly originated from between different regions, accounting for 57.42% of the total variation, followed by variation among individuals, accounting for 36.19%. Conclusion In conclusion, the results presented here indicate that the current mosquito-control strategies in Yunnan Province have effectively suppressed Ae. aegypti populations within regions. Ae. aegypti first invaded Dehong Prefecture from abroad and subsequently spread to Lincang and Xishuangbanna Prefecture. Rather than focusing primarily on preventing imported cases, greater attention should now be paid to intra-provincial spread of Aedes aegypti, particularly between port and urban areas. Abbreviations coxI Mitochondrial cytochrome c oxidase subunit 1 Ho Observed heterozygosity He Excepted heterozygosity AMOVA test Analysis of molecular variation test ne Effective number of alleles F IS Inbreeding coefficient PCA Principal component analysis DAPC Discriminant analysis of principal components Nm Number of migrants. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from the Project “the Infective Disease Prevention and Cure Project of China(No.2017ZX10303404)” Author Contribution Jian Gao, Heng-Duan Zhang and Zhi-Ming Wu: acquisition, analyses and interpretation of data, manuscript writing and revision. Dan Xing, Xiao-Xia Guo, Chun-Xiao Li, Yan-De Dong: acquisition of data, manuscript writing, experimental execution. Hong-Liang Chu1 and Tong-Yan Zhao: designed the study, manuscript revision. All authors read and approved of the final manuscript. Acknowledgement Not applicable. Data Availability The datasets supporting the conclusions of this article are included within the article。 References Mbaoma, O.C., Thomas, S.M., Beierkuhnlein C. Significance of vertical transmission of arboviruses in mosquito-borne disease epidemiology. Parasites Vectors.2025;18:137. Dechtawewat, T., Songprakhon, P., Limjindaporn T. et.al. 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Genome research. 2022; 32(3):449–58. Burri R. Evolution: Small populations, low recombination, big trouble? Current Biology. 2021;31(6), 282–4. Chen HM, Zhao H, Zhu QY, Wei HJ, et al . Genomic consequences of intensive inbreeding in miniature inbred pigs.2025; BMC Genomics, 26(1):1–13. Forneris NS, Bosse M, Gautier M, Druet T. Genomic Prediction of Individual Inbreeding Levels for the Management of Genetic Diversity in Populations with Small Effective Size. Molecular Ecology Resources. 2025;25(4): e14068. Kardos MZ, Parsons KM, Li S, et al . Inbreeding depression explains killer whale population dynamics. Nature Ecology Evolution.2023; 7(5): 675–86. Mulim H A, Campos GS, Cardoso FF, de Oliveira HR. Exploring inbreeding depression in Brazilian Angus cattle population using pedigree and genomic data. Frontiers in Genetics. 2025; 16: 1613820. Stojanovic D, Neeman T, Lacy R, Heinsohn R. Effects of non-random juvenile mortality on small, inbred populations. Biological Conservation.2022;268:109504. Konfortov BA, Bankier AT, Dear PH. An efficient method for multi-locus molecular haplotyping. Nucleic Acids Research. 2007;35(1): e6. Denker A, De LW. The second decade of 3C technologies: detailed insights into nuclear organization. Genes & development. 2016; 30(12): 1357–1382. Blot N, Clemencet J, Jourda C. Geographic population structure of the honeybee microsporidian parasite Vairimorpha (Nosema) ceranae in the Southwest Indian Ocean. Scientific reports. 2023; 13(1): 12122. Renke Lühken, Heitmann A, Jansen S, et al . Microsatellite typing of Aedes albopictus (Diptera: Culicidae) populations from Germany suggests regular introductions. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2020;104237. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 28 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor assigned by journal 17 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 09 Feb, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8827646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606569410,"identity":"6006f43c-6736-4b56-ab48-52cbc719d37d","order_by":0,"name":"Jian Gao","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Gao","suffix":""},{"id":606569411,"identity":"4369fe66-5bc7-4522-b549-161a67d8906b","order_by":1,"name":"Heng-Duan Zhang","email":"","orcid":"","institution":"Beijing Institute of Microbiology and Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Heng-Duan","middleName":"","lastName":"Zhang","suffix":""},{"id":606569413,"identity":"3bb5b47f-2711-425a-8570-26fe2bfdd65c","order_by":2,"name":"Zhi-Ming Wu","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Ming","middleName":"","lastName":"Wu","suffix":""},{"id":606569414,"identity":"ad76240b-2bc1-4b23-a808-c361827f0c65","order_by":3,"name":"Dan Xing","email":"","orcid":"","institution":"Beijing Institute of Microbiology and Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Xing","suffix":""},{"id":606569416,"identity":"4b4b76c7-afab-4923-8c99-647ecee63757","order_by":4,"name":"Xiao-Xia Guo","email":"","orcid":"","institution":"Beijing Institute of Microbiology and Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Xia","middleName":"","lastName":"Guo","suffix":""},{"id":606569419,"identity":"9f69279f-23ae-4fd8-85f6-358c6514a3c5","order_by":5,"name":"Chun-Xiao Li","email":"","orcid":"","institution":"Beijing Institute of Microbiology and Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Chun-Xiao","middleName":"","lastName":"Li","suffix":""},{"id":606569421,"identity":"59543842-ef4a-4977-bf9c-dd794cd047a2","order_by":6,"name":"Yan-De Dong","email":"","orcid":"","institution":"Beijing Institute of Microbiology and Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Yan-De","middleName":"","lastName":"Dong","suffix":""},{"id":606569422,"identity":"1db33460-931a-467e-95a1-a6a6bb386409","order_by":7,"name":"Tong-Yan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDADAwYGxgcJFTaEVfIgaWE2eHAmjTQtbJIP2w4R1mLP3nv4NU/NHXtz9sPHKhLYDjDwt3cn4LeF51yaNc+xZ8yWPWlpNxJ47jBInDm7Ab8WiRwzYx62w2wGB3LMbiRIPGMwkMglRsu/wzwG59+YFSQYHCZKi/Fj3rbDEgY3cswYEhKI0XLmjBnj3L7DBgY3niVLJBxI4yHoF/b2HuMPb74dtjc4n3zw489/NnL87b34tQABmxQPEo8HpzokwPzxBzHKRsEoGAWjYOQCAF+SSR0+OPebAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Institute of Microbiology and Epidemiology","correspondingAuthor":true,"prefix":"","firstName":"Tong-Yan","middleName":"","lastName":"Zhao","suffix":""},{"id":606569423,"identity":"333754a5-51ae-4987-98f3-df50e9e416f7","order_by":8,"name":"Hong-Liang Chu","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Hong-Liang","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2026-02-09 08:12:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8827646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8827646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104876814,"identity":"356b1b48-771c-4358-b651-68bf6e53b739","added_by":"auto","created_at":"2026-03-18 08:43:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336976,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure analysis of \u003cem\u003eAe. aegypti\u003c/em\u003e from 2016 to 2018 ((A~C) DAPC analysis of all \u003cem\u003eAe. aegypti\u003c/em\u003e populations within five regions from 2016 to 2018; (D~E) UGPMA Clustering and structure analysis of all \u003cem\u003eAe. aegypti\u003c/em\u003e populations sampled from 2016 to 2018)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8827646/v1/7f9f36f58f8b5290a1b3fa17.png"},{"id":104876908,"identity":"f1d26a1d-a596-4daf-9335-893aa690dedb","added_by":"auto","created_at":"2026-03-18 08:44:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185030,"visible":true,"origin":"","legend":"\u003cp\u003eHaplotype diversity and composition dynamics of \u003cem\u003eAe. aegypti\u003c/em\u003e populations Based on Mitochondrial \u003cem\u003eCoxI \u003c/em\u003eGene (a: evolution tree and gene mutations of the ten predominant haplotypes; b: Distribution of each haplotype among different regions; c: Network analysis of all ten haplotypes)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827646/v1/139343bdf5df7538c6f5fabe.jpeg"},{"id":104876684,"identity":"2f8892dd-df1d-4057-98e9-29b228e68480","added_by":"auto","created_at":"2026-03-18 08:43:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153804,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation similarity and dispersion Analysis of \u003cem\u003eAe. aegypti\u003c/em\u003e populations among different regions (a~c: population dispersion analysis, the number is the gene flow value between two regions; d~f: population similarity analysis, the color represents the similarity index between the populations of two regions)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827646/v1/ac3fa1b1d8520042d3ed28e3.jpeg"},{"id":104876690,"identity":"2054b2b2-8cad-432d-a163-bd39edb0af33","added_by":"auto","created_at":"2026-03-18 08:43:26","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154945,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluated evolutionary scenarios of \u003cem\u003eAe. aegypti\u003c/em\u003e invasion and dispersion in Yunnan Province using Approximate Bayesian Computation (ABC) inference. The time scale on the right of each scenario is the relative time, with 0 representing the present and increasing values going back in time. Posterior probabilities (PP) are presented for each scenario. The best-supported scenario is indicated by an asterisk.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8827646/v1/554743d1a7b9c470e4181af1.jpeg"},{"id":104876960,"identity":"5408c3b3-6335-4a2e-a6f3-bb52bf90305a","added_by":"auto","created_at":"2026-03-18 08:44:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1870347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8827646/v1/bd3e06c9-60d3-46c7-8e8e-5c73c3a7b435.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic dynamics and dispersion pattern changes of Aedes aegypti populations in Yunnan Province from 2016 to 2018","fulltext":[{"header":"Background","content":"\u003cp\u003eMosquito-borne viruses, particularly dengue virus (DENV), Zika virus (ZIKV), and Chikungunya virus (CHIKV), pose a serious threat to public health globally \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As reported, over 40% of the world\u0026rsquo;s population were affected by DENV alone and infect millions of people worldwide every year \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. To date, there is no efficient preventive vaccine available for these infectious diseases \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Thus, continuous surveillance activities and tailored control interventions are desperately needed to protect human health, especially for those living in tropical and sub-tropical regions, where continuous mosquito breeding is possible.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAedes aegypti\u003c/em\u003e (Linnaeus, 1762), an invasive species in China, is responsible for the transmission of dengue virus. Compared to \u003cem\u003eAe\u003c/em\u003e. \u003cem\u003ealbopictus\u003c/em\u003e (Skuse, 1894), \u003cem\u003eAe. aegypti\u003c/em\u003e is largely endophilic, survive in water-filled spaces, and prefer day-biting \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Outbreaks of related arboviruses can occur even at low \u003cem\u003eAe. aegypti\u003c/em\u003e densities, especially as mosquitoes breed near human dwellings \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, as an invasive species, it also possesses greater ecological niche plasticity, which potentially assists the mosquito in adapting to new environments or even replacing the native mosquito species as a dominant species, posing a greater threat to public health and safety \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. As reported, \u003cem\u003eAe. aegypti\u003c/em\u003e is strongly associated with urban environments, and its distribution seems to be affected by climate and socioeconomic factors \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Since the advancement of urbanization in China and the influence of factors such as global climate change, the distribution of Ae. aegypti in China is exhibiting a trend of expansion from south to north \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Therefore, real-time monitoring of the invasion and diffusion trend of \u003cem\u003eAe. aegypti\u003c/em\u003e in China is of great significance for the prevention and control of related arboviruses.\u003c/p\u003e \u003cp\u003eYunnan province, situated in the southwestern region of China, borders various Southeast Asian nations impacted by DENV, ZIKV and CHIKV, such as Laos, Vietnam, and Myanmar. Continuous tourism, trade, and labor export between Yunnan and these countries has led to a steady increase in imported cases of Arboviruses infection \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, the geographical and climatic conditions of Yunnan province also favor the breeding of major mosquito vector of dengue virus, \u003cem\u003eAe. aegypti\u003c/em\u003e, which may enhance the severity of dengue fever epidemic in local and even the whole country. Therefore, monitoring the population dynamics of \u003cem\u003eAe. aegypti\u003c/em\u003e in this area is also crucial for the prevention and control of dengue fever epidemics.\u003c/p\u003e \u003cp\u003eThe assessment of mosquito biodiversity, including genetic structuring and population dispersion, is the crucial step for developing and implementing appropriate strategies to control mosquito vector populations and subsequently the mosquito-borne infectious diseases \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Of all the molecular markers, Microsatellite loci and mitochondrial gene \u003cem\u003eCox I\u003c/em\u003e are the most common markers for its codominance, polymorphism, and moderate evolutionary rate, respectively.\u003cem\u003ei.e.\u003c/em\u003e, Luke \u003cem\u003eet al.\u003c/em\u003e (2014) used these markers to assess the population genetic structure of \u003cem\u003eAnopheles farauti\u003c/em\u003e and found strong gene flow barriers among \u003cem\u003eAn. farauti\u003c/em\u003e populations in the Solomon Archipelago\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Singh \u003cem\u003eet al.\u003c/em\u003e (2025) used these makers to distinguish the genetic diversity and coancestry of the captive individuals \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Noorullah \u003cem\u003eet al.\u003c/em\u003e (2025) used these makers to assess the genetic diversity among wild and captive-bred \u003cem\u003eLabeo rohita\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo date, numerous investigations have focused on the population structure of \u003cem\u003eAe. aegypti\u003c/em\u003e \u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, limited research has compared and analyzed the dynamics and dispersal patterns of mosquito populations across successive years. Thus, this study monitored the \u003cem\u003eAe. aegypti\u003c/em\u003e population in various regions of Yunnan Province in 2017 and 2018. By integrating the population data from 2016\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, the study elucidated the population dynamics of \u003cem\u003eAe. aegypti\u003c/em\u003e in Yunnan Province over a three-year period. Utilizing 9 microsatellite loci and mitochondrial \u003cem\u003ecoxI\u003c/em\u003e gene, the study assessed the impact of current dengue management strategies on the \u003cem\u003eAe. aegypti\u003c/em\u003e population. The findings aim to offer valuable insights into future dengue control initiatives.\u003c/p\u003e"},{"header":"Material methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMosquito sampling and processing\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes were collected from three different regions across Yunnan province, from 2017 to 2018. The sample information and characteristics of each sample site were given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The \u003cem\u003eAe. aegypti\u003c/em\u003e investigation was mainly conducted in indoor areas of each residential area with a focus on investigating the breeding grounds of larvae. All larvae were transferred to the lab with isolated containers and maintained at a temperature of 28℃ and 75% relative humidity with a 14:10-h (Light: Dark) photoperiod until emergency. The emerging adults were then morphologically identified under a dissecting microscope using taxonomic keys of Lu and Norbert \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, the identified female \u003cem\u003eAe. aegypti\u003c/em\u003e were then stored in 95% ethanol\u0026thinsp;\u0026minus;\u0026thinsp;80\u0026deg;C until usage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe sample information and characteristics of each \u003cem\u003eAe. aegypti\u003c/em\u003e sample site among different regions of Yunnan province, China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeographical Coordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear 2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear 2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYear 2018\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBanna prefecture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJinghong city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u0026deg;47\u0026prime;58.279\u0026Prime;E,22\u0026deg;0\u0026prime;42.894\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMenla city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u0026deg;41\u0026prime;7.843\u0026Prime;E,21\u0026deg;11\u0026prime;35.225\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaluo port\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u0026deg;1\u0026prime;58.310\u0026Prime;E,21\u0026deg;40\u0026prime;46.330\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehong prefecture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDehong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u0026deg;51\u0026prime;19.667\u0026Prime;E,24\u0026deg;1\u0026prime;4.375\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLincang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLincang city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u0026deg;24\u0026prime;10.184\u0026Prime;E,23\u0026deg;32\u0026prime;19.781\u0026Prime;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDNA isolation\u003c/b\u003e, \u003cb\u003ecoxI\u003c/b\u003e \u003cb\u003egene amplification, and microsatellite genotyping\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAt least 30 samples per site were randomly selected for the following molecular analysis. Referring to the manufacturer\u0026rsquo;s protocol, all samples were milled with a freeze grinder, and the nucleic acid was extracted using a fully automatic nucleic acid extractor with the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, USA). All DNA samples were stored at -80℃.\u003c/p\u003e \u003cp\u003eReferring to Gao \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, a 550bp fragment of \u003cem\u003ecoxI\u003c/em\u003e gene was amplified with the primers: 5\u0026prime;-GGAGGATTTGGAAATTGATTAGTTC-3\u0026prime; (\u003cem\u003eCoxI\u003c/em\u003e-Forward) and 5\u0026prime;-CCCGGTAAAATTAAAATATAAACTTC-3\u0026prime; (\u003cem\u003eCoxI\u003c/em\u003e-Reverse). Three \u0026micro;L of mosquito DNA was amplified with a TAdvanced Thermal Cycler (Analytikjena, Germany) in a 50 \u0026micro;L reaction system, containing 5 \u0026micro;L PCR buffer (TaKaRa, Japan), 4 \u0026micro;L dNTPs (2.5 mM each, TaKaRa, Japan), 1 \u0026micro;L primers (10 pmol/\u0026micro;L, TaKaRa, Japan), 0.5 \u0026micro;L PrimeSTAR HS DNA polymerase (TaKaRa, Japan) and 15.5 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003e0.The application program is set as pre-denaturation at 95\u0026deg;C for 3 min, followed by 35 cycles of denaturation at 95\u0026deg;C for 45 s, annealing at 55\u0026deg;C for 30 s, and elongation at 72\u0026deg;C for 1 min, with a final elongation at 72\u0026deg;C for 7 min. All PCR products were detected by 1.2% agarose gel electrophoresis and sequenced on an ABI 3730XL automatic sequencer (Applied Biosystems, United States).\u003c/p\u003e \u003cp\u003eReferring to Shi \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, nine microsatellite loci were selected for microsatellite genotyping. The forward primer sequence of each locus was end-labeled with different fluorescent dyes, FAM, HEX, or TAMRA. All the primers were amplified at the optimal temperature with a TAdvanced Thermal Cycler (Analytikjena, Germany), and the products were analyzed with ABI 3730XL automatic sequencer (Applied Biosystems, United States). The data was analyzed with the software Gene Marker (Version 2.2.0).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic diversity, differentiation and population clustering\u003c/h3\u003e\n\u003cp\u003eGenetic diversity indexes, such as the mean number of alleles per locus (\u003cem\u003eNa\u003c/em\u003e), observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e), expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e), and positive inbreeding coefficient (F\u003csub\u003eIS\u003c/sub\u003e), were calculated with the software popgen32 (version 1.32) Based on the mitochondrial \u003cem\u003ecoxI\u003c/em\u003e gene, the haplotype were calculated with the software Dna SP (version 6.12.03) \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. DAPC analysis of population structure of \u003cem\u003eAe. aegypti\u003c/em\u003e was performed by DAPC function in R software package \"Adegenet 2.1.3\"\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. According to the nucleotide sequence characteristics of each haplotype, genetic evolution analysis and regional distribution analysis of each haplotype were carried out by BEAST v.1.8.4\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, among which the best model of BEAST was determined by JModelTest v.2.1.10calculation \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eAMOVA test and Haplotype diversity analysis\u003c/h3\u003e\n\u003cp\u003eGenetic diversity and molecular variance of all \u003cem\u003eAe. aegypt\u003c/em\u003ei populations assessed using Analyses of Molecular Variance (AMOVA) in Arlequin v3.5\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, the nucleotide mutation network between haplotypes (TCS network) was calculated by DnaSP v.6.0 and Arlequin v.3.5.2.2 software and constructed by Network v.10.0.0.0 software \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePopulation similarity, invasion and dispersion analysis\u003c/h3\u003e\n\u003cp\u003eBased on the gene flow data of \u003cem\u003eAe. aegypti\u003c/em\u003e, the dispersion routine between 5 regions was evaluated by using the divMigrate function in R software package \"diversity v.1.9.90\"\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, where the guided replication value was set to 3 and 5, the alpha value was set to 0.05, and the gene flow method was selected for diffusion network construction, and the threshold value was set to 0.25 and 0.35, respectively. And the population similarity was displayed via R package \"Heatmap v.1.1.9\"\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo infer the invasion and dispersion history of \u003cem\u003eAe. aegypti\u003c/em\u003e in Yunnan province, the Bayesian computation methods were performed on the microsatellite dataset via DIYABC v.2.0.4\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. According to the manual of the software and the dispersion analysis results of \u003cem\u003eAe. aegypti\u003c/em\u003e, four scenarios were tested. Scenarios 1 and 3 propose that, \u003cem\u003eAe. aegypti\u003c/em\u003e invaded Dehong Prefecture and Xishuangbanna Prefecture simultaneously from abroad area, while scenarios 2 and 4 assume that, \u003cem\u003eAe. aegypti\u003c/em\u003e first invaded Dehong Prefecture from abroad area. For analysis, 1,000,000 data sets for each scenario were first simulated, resulting in 4,000,000 total simulated datasets. The relative posterior probability (PP) of each scenario was then evaluated using logistic regression on the 4,000 simulated datasets closest to the observed dataset to determine which scenario was most supported by the data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eGenetic diversity variation of\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003epopulations during 2016 to 2018\u003c/b\u003e\u003c/p\u003e \u003cp\u003eConsisting of microsatellite datasets, the ne value for \u003cem\u003eAe. aegypti\u003c/em\u003e population of each sample site ranged from 2.16 to 3.33 during 2016 to 2018. The population heterozygosity analysis results revealed that in 2016, the observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e) of \u003cem\u003eAe. aegypti\u003c/em\u003e population within Lincang City, Daluo port, and Menla City (\u003cem\u003eHo\u003c/em\u003e, 0.61\u0026thinsp;~\u0026thinsp;0.65) exceeded the expected value (\u003cem\u003eHe\u003c/em\u003e, 0.58\u0026thinsp;~\u0026thinsp;0.61). In 2017 and 2018, the \u003cem\u003eHo\u003c/em\u003e value of each \u003cem\u003eAe. aegypti\u003c/em\u003e populations was lower than expected (\u003cem\u003eHe\u003c/em\u003e). Our analysis also revealed that the inbreeding coefficient values (F\u003csub\u003eIS\u003c/sub\u003e) of \u003cem\u003eAe. aegypti\u003c/em\u003e populations from Mengla City, Daluo Port, and Lincang City in 2016 were negative (ranged from \u0026minus;\u0026thinsp;0.07 to -0.04), whereas all other \u003cem\u003eAe. aegypti\u003c/em\u003e populations displayed positive inbreeding coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the F\u003csub\u003eIS\u003c/sub\u003e values for each population showed a trend of first increasing and then decreasing over the years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity indices of \u003cem\u003eAe. aegypti\u003c/em\u003e populations sampled from five major regions of Yunnan province during 2016 to 2018 based on 9 microsatellite loci\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJinghong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaluo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLincang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*ne: Effective number of alleles [Kimura and Crow (1964)]; \u003cem\u003eHo\u003c/em\u003e: Observed heterozygosity;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eHe\u003c/em\u003e: expected heterozygosity; F\u003csub\u003eIS\u003c/sub\u003e: inbreeding coefficient\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic structure within five major regions of Yunnan province\u003c/h2\u003e \u003cp\u003ePopulation structure analysis revealed significant changes in \u003cem\u003eAe. aegypti\u003c/em\u003e population structure across five sampling sites from 2016 to 2018. Initially, in 2016, two distinct branches (K\u0026thinsp;=\u0026thinsp;2) were observed in the UGPMA tree. The \u003cem\u003eAe. aegypti\u003c/em\u003e populations from Jinghong City and Dehong clustered together, whereas those from Lincang City, Daluo Port, and Mengla City formed the other branch (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and D). In 2017, the mosquito populations were genetically divided into two branches again, with Lincang City forming a separate branch from the other four regions, where the mosquito populations from Jinghong City and Daluo Port exhibited greater compositional similarity compared to those from Mengla City and Dehong (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and E). In 2018, although the mosquito populations were once more divided into two branches, populations from Lincang City and Dehong clustered into one branch, and Mengla City, Daluo Port, and Jinghong City formed the other one. Notably, \u003cem\u003eAe. aegypti\u003c/em\u003e populations from Jinghong City and Daluo Port displayed greater similarity in composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of molecular variance (AMOVA) test of all\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003epopulations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe AMOVA analysis of all \u003cem\u003eAe. aegypti\u003c/em\u003e populations revealed that the primary sources of variation among the \u003cem\u003eAe. aegypti\u003c/em\u003e populations of different regions from 2016 to 2018 were predominantly attributed to individuals, with proportions of 80.16%, 90.38%, and 91.62%, respectively. The molecular variances within \u003cem\u003eAe. aegypti\u003c/em\u003e populations in 2017 and 2018 exhibited considerable similarity, with a notable contrast found in 2018, where the proportion of variance within individuals was notably higher at 80.36% compared to 65.15% in 2017. In contrast, in 2016, 11.26% of the molecular variations in \u003cem\u003eAe. aegypti\u003c/em\u003e populations were attributed to inter-regional differences, whereas 8.58% of the molecular variations stemmed from populations within regions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) test of all \u003cem\u003eAe. aegypti\u003c/em\u003e populations sampled from Yunnan province from 2016 to 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariance Components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariance Components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariance Components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePercentage of Variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.397Va\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161Va\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120Va\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong populations within regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302Vb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120Vb\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080Vb\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong individuals\u003c/p\u003e \u003cp\u003eWithin populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.132Vc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.739Vc\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.270Vc\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.693Vd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.908Vd\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.924Vd\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHaplotype diversity and composition dynamics of all\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003epopulations among different regions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the mitochondrial c\u003cem\u003eoxI\u003c/em\u003e gene, a total of 10 haplotypes were identified from all \u003cem\u003eAe. aegypti\u003c/em\u003e populations, named haplotype H07, H12, H05, H13, H14, H06, H04, H29, H09, and H30. Of all these haplotypes, H06 and H09 were dominant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Molecular cluster analysis of haplotype sequences showed that haplotypes H07, H12, H05, H13, H14, and H06 exhibited only 1\u0026thinsp;~\u0026thinsp;2 gene mutations with the standard \u003cem\u003eAe. aegypti CoxI\u003c/em\u003e gene sequnce, whereas haplotypes H04, H29, H09, and H30 displayed multiple gene mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Haplotype diversity analysis across different years revealed that the haplotype diversity of \u003cem\u003eAe. aegypti\u003c/em\u003e populations in Jinhong City, Dehong, Mengla City, and Lincang City gradually decreased over the years, whereas those at Daluo Port remained relatively stable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePopulation dispersion and population structure similarity analysis of\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003epopulations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTwo major dispersal cycles were discovered among different regions in 2016, \u003cem\u003ei.e.\u003c/em\u003e,1) interdiffusion between Jinghong City and Dehong (NM\u0026thinsp;=\u0026thinsp;0.49 and 0.55, respectively), and 2) circular dispersal from Mengla City to Daluo Port to Lincang City (NM\u0026thinsp;=\u0026thinsp;0.56, 0.55 and 0.57, respectively), with notable interdiffusion between Mengla City and Daluo Port (NM\u0026thinsp;=\u0026thinsp;0.56 and 1, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In 2017, \u003cem\u003eAe. aegypti\u003c/em\u003e populations displayed interdiffusion among various regions, with gene flow exceeding 0.5. Strong interdiffusion was observed between Jinghong City and Dehong (NM\u0026thinsp;=\u0026thinsp;0.60 and 0.56, respectively), Dehong and Lincang City (NM\u0026thinsp;=\u0026thinsp;0.68 and 0.58, respectively), Jinghong City and Daluo Port (NM\u0026thinsp;=\u0026thinsp;0.97 and 1, respectively), Daluo Port and Menla City (NM\u0026thinsp;=\u0026thinsp;1 and 0.56, respectively), and Dehong and Daluo Port (NM\u0026thinsp;=\u0026thinsp;0.50 and 0.58, respectively). Additionally, significant diffusion trends were noted from Mengla City to Dehong (NM\u0026thinsp;=\u0026thinsp;0.83) and from Jinghong City to Mengla City (NM\u0026thinsp;=\u0026thinsp;0.54) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). By 2018, robust population diffusion was only evident between Daluo Port and Jinhong City (NM\u0026thinsp;=\u0026thinsp;0.97 and 1, respectively) and from Mengla City to Jinghong City (NM\u0026thinsp;=\u0026thinsp;0.51) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heat map analysis results based on the population diversity index revealed notable population similarities among \u003cem\u003eAe. aegypti\u003c/em\u003e populations in different regions. In 2016, high similarities were observed between the Daluo Port and Mengla city populations, Mengla city and Lincang city populations, Daluo Port and Lincang city populations, and between the Dehong and Jinghong city populations, with similarity indices of 0.83, 0.67, 0.52, and 0.64, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). In 2017, high similarities were found between the Jinhong city and Daluo Port populations, the Dehong and Mengla City populations, and the Dehong and Lincang city populations, with similarity indices of 0.84, 0.27, and 0.32, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Similarly, in 2018, a high population similarity was observed between the Jinghong city and Daluo Port populations, Jinghong city and Mengla City populations, and Mengla City and Daluo Port populations, with similar indices of 0.90, 0.70, and 0.42, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInferring invasion and dispersion history of\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003ein Yunnan province\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, scenario 1 suggests that, \u003cem\u003eAe. aegypti\u003c/em\u003e population in Lincang City originated from the spread of mosquitoes in Xishuangbanna Prefecture (PP\u0026thinsp;=\u0026thinsp;0.292), whereas scenario 3 posited that the population in Lincang City stemmed from the spread in Dehong Prefecture (PP\u0026thinsp;=\u0026thinsp;0.054). Conversely, Scenarios 2 and 4 assumed that \u003cem\u003eAe. aegypti\u003c/em\u003e first invaded Dehong Prefecture from abroad. In this context, Scenario 2 proposes that \u003cem\u003eAe. aegypti\u003c/em\u003e initially spread to Lincang City before moving to Xishuangbanna Prefecture (PP\u0026thinsp;=\u0026thinsp;0.509). scenario 4, on the other hand, suggests that, \u003cem\u003eAe. aegypti\u003c/em\u003e first spread to Lincang City and subsequently migrated to Xishuangbanna Prefecture (PP\u0026thinsp;=\u0026thinsp;0.145). Overall, the simulation calculation results of the software were more supportive of the authenticity of Scenario 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGenetic diversity is an important genetic index to evaluate population adaptability, which can be estimated by the observed number of alleles, observed heterozygosity, and expected heterozygosity \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Many studies have shown that unintentional parental selection and inbreeding in the process of reproduction can lead to a decrease in the genetic diversity of populations \u003csup\u003e[\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In present study, the observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e) values for \u003cem\u003eAe. aegypti\u003c/em\u003e populations in the Jinghong City and Mengla City were consistently lower than the expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e) values across different years, while the inbreeding coefficient (F\u003csub\u003eIS\u003c/sub\u003e) values were all greater than 0. This pattern suggests that the \u003cem\u003eAe. aegypti\u003c/em\u003e populations in these regions have been in a prolonged state of inbreeding. Furthermore, the analysis of effective alleles indicated that the number of effective alleles in both populations initially increased before subsequently declining. Concurrently, haplotype analysis revealed a year-on-year decrease in haplotype diversity. As for Mengla City, Daluo Port, and Lincang City, the population heterozygosity over the monitoring years transitioned from \u003cem\u003eHo\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eHe\u003c/em\u003e to \u003cem\u003eHo\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eHe\u003c/em\u003e, with F\u003csub\u003eIS\u003c/sub\u003e shifting from negative to positive. These findings indicate that the \u003cem\u003eAe. aegypti\u003c/em\u003e population in Yunnan is experiencing ongoing inbreeding, which consequently leads to the decreasing in the genetic diversity of the entire population.\u003c/p\u003e \u003cp\u003eHaplotype structure is also important in understanding the evolution of a species and the dispersion of populations within it, as the rare haplotypes could also reflect a spreading population \u003csup\u003e[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. In this study, only haplotype H06 was consistently detected in \u003cem\u003eAe. aegypti\u003c/em\u003e populations from all regions across years, while H09 was detected in \u003cem\u003eAe. aegypti\u003c/em\u003e populations except in Lincang City. Other haplotypes were only detected in single regions or even single years. Specifically, haplotype H07 was detected in \u003cem\u003eAe. aegypti\u003c/em\u003e populations from Banna Prefecture across all years and was also detected in the Daluo Port population in 2018. Combined with the analysis results of the diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e populations across different regions in 2018, this also verifies the stable diffusion between the \u003cem\u003eAe. aegypti\u003c/em\u003e populations in Banna Prefecture and those at the Daluo Port. Haplotypes H14, H29, and H30 were only detected in Dehong Prefecture in 2016 and 2017, respectively, which may indicate newly invaded \u003cem\u003eAe. aegypti\u003c/em\u003e populations that have not successfully colonized. Therefore, in subsequent monitoring, we can focus on using these specific \u003cem\u003eAe. aegypti\u003c/em\u003e haplotypes to conduct real-time monitoring of the diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e populations between different locations.\u003c/p\u003e \u003cp\u003eApart from the environmental factors, such as landscape change, temperature, rainfall, \u003cem\u003eetc.\u003c/em\u003e, local mosquito control activities are also major reasons that influence the mosquito population structure significantly \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. In present study, the clustering results of \u003cem\u003eAe. aegypti\u003c/em\u003e populations reveal that, compared to those collected in 2016, the populations collected in 2017 and 2018 are genetically closer. This similarity may be attributed to the local continuous prevention and control strategies against dengue-transmitting \u003cem\u003eAedes\u003c/em\u003e mosquitoes, by which, the local \u003cem\u003eAe. aegypti\u003c/em\u003e populations have been significantly changed. Meanwhile, the continuous disinfection and sterilization of vehicles and goods entering the border have also reduced the cross-border spread of \u003cem\u003eAe. aegypti\u003c/em\u003e. The genetic clustering for \u003cem\u003eAe. aegypti\u003c/em\u003e populations closely correspond with the findings of population diversity, which showed that the \u003cem\u003eAe. aegypti\u003c/em\u003e population in Yunnan in 2016 had a lower inbreeding coefficient due to extensive gene exchange with exogenous \u003cem\u003eAe. aegypti\u003c/em\u003e populations, resulting in a heterozygous state. However, the \u003cem\u003eAe. aegypti\u003c/em\u003e populations in 2017 and 2018 displayed moderate inbreeding state.\u003c/p\u003e \u003cp\u003eThis study discovered that from 2016 to 2018, the diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e populations among different regions in Yunnan exhibited a trend of initial increase followed by a decrease, which may be attributed to the implementation of local dengue fever prevention and control strategies. Following the local dengue epidemic caused by imported cases in Yunnan in 2015, Yunnan gradually implemented control measures against \u003cem\u003eAe. aegypti\u003c/em\u003e. The population inbreeding coefficient and population diffusion map from 2016 to 2017 showed that although the invasion of \u003cem\u003eAe. aegypti\u003c/em\u003e from outside the country into the interior was reduced, the mutual diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e among different regions in Yunnan was still not effectively controlled. Therefore, by 2017, the \u003cem\u003eAe. aegypti\u003c/em\u003e populations among the five regions in Yunnan were basically in a state of mutual diffusion. In 2017, the government introduced a comprehensive local prevention and control plan for Ae. aegypti (the \"Xishuangbanna Dengue Fever Prevention and Control Propaganda Work Plan (2017)\"), which effectively controlled the local diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e populations. Consequently, by 2018, the \u003cem\u003eAe. aegypti\u003c/em\u003e populations were only diffusing between Banna and Dehong prefecutres. Meanwhile, the AMOVA analysis is based on the mitochondrial gene \u003cem\u003eCoxI\u003c/em\u003e of \u003cem\u003eAe. aegypti\u003c/em\u003e also confirmed this hypothesis, indicating that the variation in \u003cem\u003eAe. aegypti\u003c/em\u003e populations across Yunnan in 2016 and 2017 mainly originated from within the \u003cem\u003eAe. aegypti\u003c/em\u003e populations themselves, accounting for 79.48% and 74.18% of the total variation, respectively. However, by 2018, the variation in \u003cem\u003eAe. aegypti\u003c/em\u003e populations in Yunnan mainly originated from between different regions, accounting for 57.42% of the total variation, followed by variation among individuals, accounting for 36.19%.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the results presented here indicate that the current mosquito-control strategies in Yunnan Province have effectively suppressed \u003cem\u003eAe. aegypti\u003c/em\u003e populations within regions. \u003cem\u003eAe. aegypti\u003c/em\u003e first invaded Dehong Prefecture from abroad and subsequently spread to Lincang and Xishuangbanna Prefecture. Rather than focusing primarily on preventing imported cases, greater attention should now be paid to intra-provincial spread of Aedes aegypti, particularly between port and urban areas.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ecoxI\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitochondrial cytochrome c oxidase subunit 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObserved heterozygosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExcepted heterozygosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMOVA test\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of molecular variation test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ene\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffective number of alleles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInbreeding coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiscriminant analysis of principal components\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumber of migrants.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the Project \u0026ldquo;the Infective Disease Prevention and Cure Project of China(No.2017ZX10303404)\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJian Gao, Heng-Duan Zhang and Zhi-Ming Wu: acquisition, analyses and interpretation of data, manuscript writing and revision. Dan Xing, Xiao-Xia Guo, Chun-Xiao Li, Yan-De Dong: acquisition of data, manuscript writing, experimental execution. Hong-Liang Chu1 and Tong-Yan Zhao: designed the study, manuscript revision. All authors read and approved of the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article。\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMbaoma, O.C., Thomas, S.M., Beierkuhnlein C. Significance of vertical transmission of arboviruses in mosquito-borne disease epidemiology. Parasites Vectors.2025;18:137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDechtawewat, T., Songprakhon, P., Limjindaporn T. \u003cem\u003eet.al.\u003c/em\u003e Role of Human Heterogeneous Nuclear Ribonucleoprotein C1/C2 in Dengue Virus Replication. 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Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2020;104237.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aedes aegypti, genetic structure, invasion, dispersion, and Microsatellite","lastPublishedDoi":"10.21203/rs.3.rs-8827646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8827646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAedes aegypti\u003c/em\u003e is the major vector for massive mosquito-borne viruses, including Dengue, ZIKV, etc., and threatens human health world-widely. The absence of efficient vaccine for those insect-borne diseases highlights the importance of monitoring of genetic changes of the \u003cem\u003eAe. aegypti\u003c/em\u003e populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe genetic variation and population structure of \u003cem\u003eAe. aegypti\u003c/em\u003e populations collected from three regions of Yunan province in China during 2016 to 2018 were investigated with 9 microsatellite loci and the mitochondrial \u003cem\u003ecoxI\u003c/em\u003e gene.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2016 to 2018, the genetic diversity of \u003cem\u003eAe. aegypti\u003c/em\u003e populations in Yunnan province displayed a trend of initial increase followed by a decrease. The degree of inbreeding within the populations gradually shifted from heterozygous to moderately inbred. The clustering results indicated that, compared to the populations collected in 2016, those collected in 2017 and 2018 were genetically closer. Ten \u003cem\u003ecoxI\u003c/em\u003e haplotypes were detected, haplotypes H06 and H09 were detected nearly in all regions, while the others were only detected in single region or even single year. The population diffusion analysis indicated that the diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e populations among different locations exhibited a trend of first increasing and then decreasing. Meanwhile, the AMOVA analysis results also revealed that the source of variation in the Yunnan \u003cem\u003eAe. aegypti\u003c/em\u003e populations gradually shifted from mainly inter-individual to mainly inter-regional. The evolutionary scenario inference of the invasion and diffusion of \u003cem\u003eAe. aegypti\u003c/em\u003e indicated that the mosquito invaded and colonized Dehong Prefecture from Southeast Asian, and then diffused to Lincang City, and finally to Xishuangbanna Prefecture.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe results presented here indicate that the current mosquito-control strategies in Yunnan Province have effectively suppressed \u003cem\u003eAe. aegypti\u003c/em\u003e populations within regions. Rather than focusing primarily on preventing imported cases, greater attention should now be paid to intra-provincial spread of \u003cem\u003eAe. aegypti\u003c/em\u003e, particularly between port and urban areas.\u003c/p\u003e","manuscriptTitle":"Genetic dynamics and dispersion pattern changes of Aedes aegypti populations in Yunnan Province from 2016 to 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:42:46","doi":"10.21203/rs.3.rs-8827646/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T13:06:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T05:10:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T07:34:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245357856435460996965453134919036098513","date":"2026-03-17T17:04:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221121687674836432889132224406433336012","date":"2026-03-16T04:09:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89782255952664257347685112551977943025","date":"2026-03-15T17:33:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-13T17:16:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T09:50:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-11T07:31:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2026-02-09T07:56:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"624ff237-edb5-4c5a-a6ca-46f95fd626d9","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T13:53:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:42:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8827646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8827646","identity":"rs-8827646","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}