Effects of regional climatic conditions on the wing morphometrics of the dengue vector Aedes aegypti (Diptera: Culicidae) in India

Effects of regional climatic conditions on the wing morphometrics of the dengue vector Aedes aegypti (Diptera: Culicidae) in India | 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 Effects of regional climatic conditions on the wing morphometrics of the dengue vector Aedes aegypti (Diptera: Culicidae) in India Gaurav Sharma, Rakesh Bhutiani, Devojit Kumar Sarma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7937635/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Aedes aegypti (Linnaeus, 1762), a major arboviral vector of global importance, demonstrates high adaptability across diverse environments. In India, Ae. aegypti is widespread across the country, thriving in highly diverse environmental conditions. Given its genetic, behavioral, and physiological variability, this study explores whether environmental factors also influence phenotypic traits such as wing morphology across the diverse climatic conditions of India. Right wings from 256 female Ae. aegypti specimens across 12 populations in five major climatic regions of India, viz. , Arid, Semi-Arid, Tropical Wet, and Dry, Mountain, and Humid Subtropical, were analyzed for morphometric variation. Significant differences in wing centroid size (CS) and shape were observed among both the populations and climatic regions. Arid region (3.95 ± 0.56) and Nagpur population (4.49 ± 0.31) exhibited the largest wings, while Mountain region (Srinagar population) showed the smallest (1.93 ± 0.11). Canonical Variate Analysis (CVA) revealed significant wing shape differences among both populations and regions, with the Semi-Arid region and Kota population showing distinct divergence. Cross-validated reclassification demonstrated high accuracy, with 79% of population-level and 90% of region-level comparisons exceeding 50%. Redundancy Analysis (RDA) showed that wing size was positively influenced by diurnal temperature range, root-soil moisture, and latitude, while precipitation and surface moisture had negative effects. Neighbor-Joining trees highlighted phenetic clustering influenced more by local environmental conditions than macroclimatic zones. Conclusively, this study highlights the role of environmental variation in shaping wing morphometry, providing insights into population differentiation and aiding region-specific vector control strategies. Aedes aegypti dengue wing morphometrics size shape climate region Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Dengue fever, the most prevalent mosquito-borne illness, has witnessed a substantial increase in reported cases, with a 10-fold surge observed from 2000 to 2019 globally (WHO 2023). In India, more than 0.2 million cases have been reported in the year 2023 (NCVBDC 2024). Aedes aegypti (Linnaeus, 1762), is the primary vector, responsible for this deadly disease. It is also the potential vector of several other epidemiologically important diseases including Chikungunya, and Zika in India (NCVBDC 2024). Historically, Ae. aegypti preferred non-human hosts and inhabited tropical forests, with larvae developing in tree holes. However, as the human population expanded, Ae. aegypti populations underwent evolutionary changes to adapt to human habitats. It is believed that this mosquito species has invaded other parts of the world through human movements and trades from Central Africa (Tabachnick 1991). Consequently, diverse ecological and geographical conditions lead to changes in Ae. aegypti mosquito in context to their morphology, behaviour, and genetics (Louise et al. 2015; Sumitha et al. 2023; Sharma et al. 2023). Vector control is considered the most effective management strategy to prevent the expansion of vector-borne diseases, making an understanding of population dynamics important. While genetic and behavioural studies have provided valuable insights into Ae. aegypti population dynamics and vector potential (Barrera et al. 2011; Grech et al. 2015; De et al. 2022; Sharma et al. 2023; Sumitha et al. 2023), morphological traits serve as an integrative phenotype, reflecting the combined effects of genetic variation, developmental plasticity, and environmental pressures. This approach enables the detection of subtle morphological shifts associated with local adaptation, ecological divergence, and population structure, offering a cost-effective and evolutionarily informative tool for vector monitoring and comparative morphology (Lorenz et al. 2017). Morphological traits, especially wing size and shape, are increasingly recognized as reliable bioindicators of environmental stress and population-level variation in insects (Hoffmann et al. 2002). Geometric morphometric (GM) analysis is a powerful, cost-effective tool that allows precise quantification of shape variation, revealing both inter- and intraspecific differentiation (Lorenz et al. 2017). In mosquitoes, wing morphology is directly linked to flight performance, host-seeking ability, dispersal potential, and susceptibility to viral infection, collectively influencing vector competence and transmission dynamics (Scott et al. 2000; Alto et al. 2008; Yeap et al. 2013). Geometric morphometric approaches have been widely applied to study morphological variation influenced by sex (Christe et al. 2016), geography (Hounkanrin et al. 2023), phylogeny (Lorenz et al. 2015), ecological associations (Carreira et al. 2011), temperature (Aytekin et al. 2009), demographic factors (Wilk-da-Silva et al. 2018), altitude (Leyton Ramos et al. 2020) and landscape structure (Hounkanrin et al. 2023). These studies consistently demonstrate that wing morphometrics serve as a non-invasive, high-resolution marker of local adaptation and environmental effects in Aedes species. Although most of the Indian cities experience a tropical to subtropical climate, there exists a local variation in topography, and land use contributing to micro-climatic heterogeneity that influences Ae. aegypti abundance and dengue epidemiology (Sarma et al. 2022). Due to this environmental heterogeneity in India, five different geo-climatic regions can be identified: Arid, Semi-arid, Tropical wet and dry, Humid sub-tropical, and Mountain (Beck et al. 2018). It is evident that Ae. aegypti have invaded all these climatic regions, with documented genetic (Sumitha et al. 2023) and behavioral differences (Sharma et al. 2023) across regions. Despite some genomic and ecological studies, morphometric variation among Indian populations concerning different climatic conditions remains largely unexplored. Given the evolutionary and ecological significance of wing morphology in Ae. aegypti , a comparative morphometric analysis across ecological gradients can provide valuable insight into how local environmental factors shape phenotypic traits in vector populations. Therefore, this study investigates the wing morphometrics of Ae. aegypti collected from 12 distinct locations across five climatic regions in India. By applying geometric morphometric methods, we aim to evaluate the extent of morphological variation among these populations and assess whether wing traits reflect ecological divergence and potential adaptive differentiation. This work offers a morpho-ecological perspective on vector variability and contributes to understanding the functional morphology of disease vectors in changing environments. Material and Methods Study sites and sample populations Aedes aegypti specimens were collected between November 2022 and November 2024 from 12 geographically distinct locations across India i.e., Jodhpur, Kota, Sri Ganganagar, Srinagar, Hoshiarpur, Lucknow, Nagpur, Raipur, Kolkata, Visakhapatnam, Guwahati, and Itanagar —representing five distinct climatic regions: Tropical wet and dry, Arid, Semi-arid, Humid sub-tropical and Mountain (Fig. 1). Sampling was conducted irrespective of seasons over the two-year period due to logistical constraints and the availability of a single insect collector. Detailed information on sampling sites for each population is provided in Supp. File 1. Mosquitoes were sampled within human dwellings and their surroundings in the localities of each site. Sample Collection Field surveys were conducted at up to 20 distinct sites in each population, maintaining a minimum distance of 500 meters between sites to minimize the likelihood of sampling closely related individuals. Previous studies indicate that, Ae. aegypti dispersal is typically limited, with a meta-analysis estimating a weighted mean distance travelled of approximately 106 m (Moore and Brown 2022). However, mark–release–recapture experiments have shown that ovipositing females may travel much farther—up to 840 m in urban environments—when searching for multiple oviposition sites (Reiter et al. 1995). Considering this evidence, a minimum separation of 500 m between sampling sites was adopted as a practical compromise to reduce the probability of pseudo replication. Pupae stages of Ae. aegypti was collected from approximately 2 to 17 of these sites per population, depending on the availability of breeding habitats, positivity rates, and accessibility. At each positive site, pupae were collected using a glass pipette from multiple larval habitats, primarily cement tanks and discarded tires. To reduce the likelihood of sampling siblings, only one individual was used from each distinct breeding habitat. All collected specimens were transported to the laboratory at ICMR–National Institute for Research in Environmental Health (ICMR-NIREH), Bhopal, Madhya Pradesh, and reared to adulthood under standardized laboratory conditions (temperature: 27 ± 2 °C, relative humidity: 75 ± 5%, and photoperiod: 12:12 h light: dark). Emerged adults were anesthetized using cotton soaked in 0.5ml of chloroform and identified morphologically using standard taxonomic keys (Tyagi et al. 2015). All adult specimens were stored under 4 ºC for morphometric study. To avoid sampling siblings, only one emerged female mosquito per developmental habitat was used for wing morphometric analysis. No adult mosquitoes were collected directly from the field during this study. A total of 256 adult female mosquitoes were included in the final wing morphometric analysis, averaging approximately 21 individuals per population. Previous studies in mosquito morphometrics have also used similar or smaller sample sizes per population to effectively capture variation in wing shape and size (Garzón and Schweigmann 2018). Wing preparation The right wings of female Ae. aegypti mosquitoes were removed from the thorax and mounted on a microscope slide (15 mm x 15 mm) with a coverslip. Each wing was then photographed under 40x magnification using ImageView software (version x64, 4.11.18012.20201123) with STEMI 305 stereomicroscope. Landmark digitisation A total of 18 landmarks were digitized using ImageJ ver. 1.54d (Schneider et al. 2012) software following methods described by Hounkanrin et al. (2023). The wing picture and digitization of landmarks were done by one author (GS) and repeated three times to minimize error. Total 256 wing specimens of female Ae. aegypti (Jodhpur - 22, Kota - 8, Sri Ganganagar - 27, Srinagar - 15, Hoshiarpur - 24, Lucknow – 17, Nagpur - 27, Raipur - 13, Kolkata - 27, Visakhapatnam - 20, Guwahati - 24, and Itanagar – 32) were used. The representation of 18 landmarks and their coordinates are provided in Fig. 2a and Supp. File 1 respectively. Wing size and shape estimation The centroid size (CS) representing wing size, was calculated from raw wing coordinates data. Centroid size is defined as the square root of the total squared distances measured from the centroid to each landmark, and it can be employed as a proxy for wing size (Dujardin 2008). Coordinates were imported into the MorphoJ ver. 1.08.01 (Klingenberg 2011) in Tab delimited format. These coordinates were aligned by performing Procrustes superimposition (Fig. 2b) to visualize the position of each landmark for each specimen using MorphoJ ver. 1.08.01 (Klingenberg 2011). To statistically compare the mean CS among specimens from different locations and regions, Tukey's HSD test for multiple comparisons of means was performed using GraphPad Prism version 8.0. The assessment of wing shape involved the importation of wing coordinates in a text-delimited format using MorphoJ ver. 1.08.01 (Klingenberg 2011). To conduct a comparative analysis, Procrustes ANOVA was performed using the MorphoJ ver. 1.08.01. Following this, Canonical Variate Analysis (CVA) (70% confidence) was applied to visually represent shape variations based on Procrustes coordinates. Additionally, Mahalanobis distances were computed using MorphoJ ver. 1.08.01 software to further quantify the differences in wing shape. To ensure the species confirmation, all the specimens were analysed morphologically as well as at the molecular level following the method described by Higa et al. (2010) targeting the ITS region of the genus Aedes. Phenetic relationship Neighbour-Joining (NJ) tree was constructed with 100 bootstrap replicates based on Mahalanobis distances obtained through pairwise comparison of wing specimens via CVA using PAST software v.4.03 (Hammer et al. 2001) to illustrate the phenetic relationships among the populations and, among the climatic regions. Allometric effect To understand the influence of wing size on wing shape (allometry), the multivariate regression of the Procrustes coordinates (dependent variable) against log CS (independent variable) was analysed using a permutation test with 10,000 randomizations using MorphoJ ver. 1.08.01. Effect of environmental factors on wing size Principal Component Analysis (PCA) was employed at both the population level and the climatic region level to elucidate the influence of environmental variables on wing centroid size variation in Ae. aegypti across India. The environmental variables include the mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), relative humidity (RH), precipitation, latitude, and various measures of soil moisture (surface at 5 cm, root zone at 100 cm and profile depth) (Supp. Table S1). These environmental parameters for all the sampled locations were retrieved from the National Aeronautics and Space Administration (NASA) Langley Research Centre Prediction of Worldwide Energy Resource Project (https://power.larc.nasa.gov/data-access-viewer/ ). Results The present study conducted wing morphometric analyses using a total of 256 wing specimens obtained from 12 distinct populations of Ae. aegypti (Fig. 1 and Supp. File 1) in India. The mean CS estimated from the coordinates of 18 wing landmarks, exhibited a significant difference among the populations ( F ratio = 236.3, p = <0.0001) as well as among the climatic regions ( F ratio = 22.29, p = <0.0001). The Nagpur population showed the highest CS (4.49 ± 0.31 mm), while the Srinagar population presented the lowest CS (1.93 ± 0.11 mm) (Fig. 3a, Table 1). Similarly, the Arid region showed the highest CS (3.95 ± 0.56 mm) while the Mountain region displayed the lowest CS (1.93 ± 0.11 mm) (Fig. 3b, Table 1). Canonical Variate Analysis (CVA), validated through Procrustes ANOVA, revealed significant differences in wing shape both among populations ( F = 2.71, p < 0.0001) and among climatic regions ( F = 1.75, p = 0.01). At the population level, CVA accounted for a total of 36.31% of the shape variance, with CV1 explaining 20.94% and CV2 contributing 15.37% of the variation (Fig. 4a). Most populations exhibited varying degrees of overlap, indicating shared morphological traits. However, the Kota (KT) population was distinctly separated along CV1, suggesting a significantly divergent wing morphology relative to other populations. At the climatic region level, the analysis explained a higher proportion of the total variation (72.81%), with CV1 accounting for 41.51% and CV2 for 31.29% (Fig. 4b). Although considerable overlap was observed among the regions as well as populations. The Semi-Arid (SAD) region and Kota (KT) population exhibited the widest dispersion, indicating high variability in wing morphology. Cross-validated pairwise reclassification based on population-level comparisons demonstrated a high degree of wing shape differentiation ( p < 0.0001), with classification accuracies ranging from low (VP vs. SN) to high (JD vs. GW; SN vs. HP). Approximately 79% of population-level comparisons exceeded the 50% accuracy threshold (Table 2). Similarly, reclassification analysis across climatic regions ( p < 0.0001) revealed that 90% of pairwise comparisons surpassed the 50% accuracy threshold, with classification accuracies ranging from low (TWD vs. MTN) to high (HST vs. ARD) (Table 3). These results provide strong statistical evidence for wing shape divergence both among the population and among the climatic regions. The Neighbor-Joining (NJ) trees constructed using Mahalanobis distances derived from canonical variate analysis (CVA) effectively illustrated the phenetic relationships among Ae. aegypti populations (Fig. 5a) and among different climatic regions of India (Fig. 5b). At the population level, two major clusters were observed: one comprising KT, LK, VP, RP, HP, and JD, and another including GW, IT, KK, SG, SN, and NG. The subgrouping of KT and LK showed the strongest bootstrap support (91%), followed by VP-RP (36%) and HP-JD (48%). Within the second cluster, distinct pairings were observed between GW and IT (55%) and KK and SG (27%), with SN and NG forming the terminal branch of this group. Similarly, at the climatic region level, two primary clusters were evident. One comprises HST, TWD, and ARD regions while the other includes MTN and SAD regions. Within the first cluster, HST and TWD formed a well-supported subgroup (bootstrap support: 82%), followed by the inclusion of ARD. The second cluster grouped MTN and SAD with moderate bootstrap support (49%). Multivariate regression of the Procrustes coordinates on log CS showed an allometric effect of wing size on wing shape. (Population: 5.89%, p < 0.0001; Region: 2.40%, p < 0.0001). We excluded this effect from the subsequent analysis. Redundancy Analysis (RDA) was conducted to examine the influence of environmental variables on the mean wing centroid size (mean CS) of Ae. aegypti at both population (Fig. 6a) and climatic region levels (Fig. 6b). At the population level, the first two principal components (PCA1: 82.1%, PCA2: 17.9%) accounted for the total variance. Mean centroid size showed strong positive associations with root soil moisture (100 cm), diurnal temperature range (DTR), and latitude (N). Profile soil moisture (StoB) exhibited a moderate negative influence on mean CS, along with precipitation (mm/day) and surface soil moisture (5 cm), which projected in opposite directions. In contrast, at the climatic region level, PCA2 alone explained most of the variance (99.8%). Mean CS aligned distinctly along PCA1, while most environmental variables including precipitation, and soil moisture at multiple depths (5 cm, 100 cm, StoB), showed strong projections along PCA2. Notably, precipitation and surface soil moisture exhibited a strong inverse association with wing size, indicating that mosquitoes from wetter regions tend to have smaller wings, as observed in the Mountain region and Srinagar population. Discussion Aedes aegypti exhibits a remarkable adaptability to anthropogenic modifications, thriving across a diverse range of environmental conditions. To thrive in such diverse environmental settings, Ae. aegypti must overcome selective pressures. Previous research has highlighted the varied climatic conditions in India (Beck et al. 2018) and their impacts on mosquitoes, influencing their genetics (Sumitha et al. 2023), physiology (Sharma et al. 2023), and morphology (Hounkanrin et al. 2023). Expanding the existing knowledge, the present study analysed the wing morphometrics of 12 Ae. aegypti populations from five different climatic regions of India to understand the influence of native environmental conditions on wing size and shape. The climatic regions included Arid (Jodhpur, Sri Ganganagar), Semi-arid (Kota, Hoshiarpur), Tropical wet and dry (Nagpur, Raipur, Visakhapatnam, Kolkata), Mountain (Srinagar), and Humid sub-tropical (Lucknow, Guwahati, Itanagar). This regional classification reflects the broad spectrum of ecological conditions that shape Ae. aegypti populations throughout India. Wing size is commonly used as a proxy for overall mosquito size (Dujardin 2008), and it can influence various physiological and behavioural aspects of mosquitoes (Yeap et al. 2013). Larger mosquitoes, characterized by greater wing size, often exhibit distinct metabolic profiles, enhanced blood-feeding capacities, and improved flight capabilities compared to their smaller-size mosquitoes (Yeap et al. 2013). These factors can collectively affect the mosquito's ability to transmit diseases. For example, larger mosquitoes are less susceptible to viral infections, potentially influencing the dynamics of virus transmission within mosquito populations (Alto et al. 2008). Therefore, wing morphometric studies have practical implications for formulating more effective vector management strategies in specific areas, emphasizing the importance of considering local environmental conditions in such interventions. The present study found that mosquitoes from arid regions (Jodhpur and Sri Ganganagar) exhibited larger wing sizes (3.95 ± 0.13 mm) among all regions. This phenomenon can be attributed to the effects of high temperatures, which accelerate larval metabolic rates and increase mortality during development. Consequently, the surviving larvae face reduced competition and have access to greater nutritional resources, promoting larger body and wing sizes. These observations align with the findings of Mohammed et al. (2011), who demonstrated that Ae. aegypti larvae reared under higher, fluctuating temperatures (25 ºC-35 ºC) in Trinidad, West Indies, developed into larger adults with bigger wings. Conversely, mosquitoes from the mountain region (Srinagar) showed shorter wing size (1.92 ± 0.24 mm). This can be explained as lower temperature conditions that prevail in mountainous areas, slow down the development rate and results in prolonged exposure to suboptimal conditions, which can limit growth. Additionally, colder temperatures can reduce metabolic efficiency in mosquito larvae, affecting their ability to assimilate and utilize nutrients effectively. These trends are consistent with previous studies indicating that low temperatures reduce metabolic performance in larvae and affect nutrient utilization (Carrington et al. 2013). Moreover, while temperature appears to be a key driver of wing size variation, other environmental factors such as humidity, soil moisture, precipitation, larval density, food availability, and urbanization may also contribute to the observed differences (Alto and Juliano 2001; Pautzke et al. 2024; Jirakanjanakit et al. 2007; Oliveira-Christe et al. 2023; Hounkanrin et al. 2023). Mohammed et al. (2011) similarly reported no significant linear correlation between centroid size and temperature, reinforcing the idea that wing size is shaped by multifactorial influences. This aspect was further supported by the RDA analysis in the present study which shows mean CS was positively affected by root soil moisture (100 cm), diurnal temperature range (DTR), and latitude (N) at the population level. Whereas, variables such as profile soil moisture (StoB), precipitation (mm/day), and surface soil moisture (5 cm) were negatively associated with wing size at the population level. This indicates that higher DTR and deeper soil moisture may support greater mosquito growth (Lockaby et al. 2016). Similarly, precipitation and surface soil moisture showed an inverse effect on mean CS at the climatic region level, which further suggests that mosquitoes from wetter regions tend to have smaller wings. These findings collectively reinforce that no single factor, such as temperature, entirely determines wing size; rather, it is the complex interplay of multiple ecological variables that shape morphometric outcomes. Although wing size offers valuable insights, it is recognized as being more vulnerable to environmental variations (Lorenz et al. 2017). For example, in Ae. albopictus populations from Thailand, wing size was found to be influenced by climatic conditions (Vargas et al. 2010). This susceptibility necessitates caution when interpreting wing size data, as it may reflect short-term environmental effects rather than stable population-level traits. In contrast, wing shape is more robust against such environmental fluctuations and is considered a better indicator of heritable, intraspecific, and regional differences (Carvajal et al. 2016; Rodríguez-Zabala et al. 2016; Krtinić et al. 2016). In the present study, the morphospace analysis, using Canonical Variate Analysis (CVA), reveals statistically significant differences in wing shape among the populations as well as across climatic regions. Notably, the Semi-Arid region (Kota population) occupied a larger morphospace than the other regions, indicating a higher degree of shape variation. This distinctiveness suggests a unique environmental influence or possible local adaptation. In contrast, several other populations and regions exhibited overlapping morphospace, reflecting similarity in wing shapes likely driven by shared environmental conditions. These morphological distinctions were further supported by a cross-validated reclassification test, which demonstrated that 79% of pairwise population comparisons and 90% of region-wise comparisons had classification accuracies above 50%. Such accuracy levels highlight the reliability of wing shape as a marker for population differentiation and suggest climatic variability plays a significant role in shaping population structure in Ae. aegypti . Interestingly, comparable patterns have also been reported in other parts of the world. A study from Vila Toninho, Brazil (Prado et al. 2022), reported significant seasonal and spatial variation in the wing shape of Ae. aegypti females. The most pronounced differences occurred between winter and spring, as well as among sites during the summer, corresponding with fluctuations in dengue transmission intensity. These changes in wing shape were interpreted as signs of microevolution, likely driven by localized environmental pressures such as temperature shifts and precipitation variability. Taken together, these findings reinforce the idea that wing shape variation in Ae. aegypti reflects underlying ecological and evolutionary processes acting at both regional and local scales. The Neighbor-Joining (NJ) trees derived from Mahalanobis distances revealed clear patterns in the phenetic relationships among Ae. aegypti populations and their corresponding climatic regions. At the population level, two primary clusters emerged: the first included KT, LK, VP, RP, HP, and JD, while the second comprised GW, IT, KK, SG, SN, and NG. Interestingly, populations from climatically distinct regions such as JD (Arid) and HP (Semi-Arid), and RP and VP (TWD), clustered together in the first group. These associations suggest that morphological similarity is not strictly governed by macroclimatic classification but may instead be influenced by shared micro-environmental conditions, such as urbanization level, breeding habitat types, and anthropogenic disturbances. Similarly, the region-level NJ tree revealed two major clades: one grouping HST, TWD, and ARD regions, and the other clustering MTN and SAD. The inclusion of ARD with more humid regions (HST and TWD) indicates convergence driven by comparable ecological pressures in urban environments, such as intermittent water availability, artificial containers, and temperature fluctuations. Together, the population- and region-level trees underscore that wing shape variation in Ae. aegypti is shaped by a complex interplay of local environmental and anthropogenic factors, rather than climatic region alone. However, while these clustering patterns provide ecologically plausible interpretations, their reliability warrants caution, as several groupings were supported by low to moderate bootstrap values, indicating limited statistical robustness. Further genetic studies may shed more light on this regard. Conclusion The present study highlights significant morphological variation in Aedes aegypti across 12 geographically diverse populations and five major climatic regions of India. Notable differences in wing size and shape were observed, with the largest wings recorded in the Nagpur population and the smallest in Srinagar. Regionally, mosquitoes from the Arid region exhibited larger wings, while those from the Mountain region had smaller ones. However, these patterns were not solely attributable to temperature. Instead, the observed variation appears to result from a multifactorial interplay of environmental conditions. The pronounced wing shape divergence, particularly in the Semi-Arid region (Kota), further supports the role of local adaptation. Overall, this study demonstrates that geometric morphometric analysis is a valuable tool for detecting intraspecific variation and understanding ecological influences on mosquito populations, offering insights that can enhance vector surveillance and control strategies. Declarations Data availability statement Derived data supporting the findings of this study are available from the corresponding author on request. Acknowledgments The authors thank Dr. Surya Singh, Scientist-B, ICMR-National Institute for Research in Environmental Health, Bhopal, India, for providing technical support. Author Contributions GS: Conceptualization, Data Curation, Resources, Software, Validation, Writing – original draft. RB: Supervision, Resources, Software, Writing - review & editing. DKS: Conceptualization, Supervision, Funding acquisition, Project administration, Writing –original draft. Funding sources The study was funded by the Indian Council of Medical Research under the scheme ICMR-SRF (No. Fellowship/106/2022-ECD-II). Ethical statement The study does not require any ethical approval. Conflict of Interest The authors declare no conflict of interest. References Alto BW, Juliano SA (2001) Precipitation and temperature effects on populations of Aedes albopictus (Diptera: Culicidae): implications for range expansion. 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Accessed on 21 December 2023 Yeap HL, Endersby NM, Johnson PH, et al. (2013) Body size and wing shape measurements as quality indicators of Aedes aegypti mosquitoes destined for field release. Am J Trop Med Hyg 89:78. https://doi.org/10.4269/ajtmh.12-0719 Tables Table 1: Population and climatic region wise mean centroid sizes (CS) of Aedes aegypti in India. Bold numbers represent the mean centroid size (CS) and the total number of Aedes aegypti mosquitoes for each climatic region. Location & climatic regions Latitude (N) Longitude (E) Mean CS±SD N (no of wing analyzed) Visakhapatnam 17.6868 83.2185 4.09 ± 0.29 20 Kolkata 22.5744 88.3629 2.00 ± 0.08 27 Nagpur 21.1458 79.0882 4.49 ± 0.31 27 Raipur 21.2514 81.6296 4.41 ± 0.37 13 Tropical Wet and Dry 3.61 ± 1.19 87 Jodhpur 26.2389 73.0243 3.55 ± 0.30 22 Sri Ganganagar 29.9094 73.88 4.29 ± 0.51 27 Arid 3.95 ± 0.56 49 Kota 25.2138 75.8648 3.71 ± 0.92 8 Hoshiarpur 31.5143 75.9115 2.32 ± 0.14 24 Semi-Arid 2.67 ± 0.76 32 Itanagar 27.0844 93.6053 2.09 ± 0.13 32 Guwahati 26.1158 91.7086 4.38 ± 0.39 24 Lucknow 26.8467 80.9462 2.58 ± 0.20 17 Humid Sub tropical 2.96 ± 1.05 73 Srinagar 34.0837 74.7973 1.93 ± 0.11 15 Mountain 1.93 ± 0.11 15 Table 2. Pairwise cross-validated reclassification (%) among the different populations of India. Values below the diagonal correspond to the proportion of Group 1 mosquito specimens correctly identified after comparison with Group 2. Values above the diagonal correspond to the proportion of Group 2 mosquito specimens correctly identified after comparison with Group 1. ( p -value < 0.0001). (Abbreviation: GW - Guwahati, HP - Hoshiarpur, IT - Itanagar, JD - Jodhpur, KK - Kolkata, KT - Kota, LK - Lucknow, NG - Nagpur, RP - Raipur, SG – Sri Ganganagar, SN - Srinagar, VP - Visakhapatnam). Group 2 Reclassification test GW HP IT JD KK KT LK NG RP SG SN VP Group 1 GW - 67 53 68 52 38 59 63 31 85 47 65 HP 67 - 66 59 81 50 59 59 54 67 87 60 IT 58 67 - 64 59 50 65 67 38 70 47 70 JD 88 58 66 - 67 50 53 70 31 78 47 50 KK 63 63 75 73 - 38 71 56 54 56 47 60 KT 54 63 66 50 59 - 82 52 54 41 53 60 LK 58 67 66 59 78 50 - 63 38 70 33 55 NG 46 50 69 73 67 50 59 - 46 59 60 60 RP 46 54 56 45 70 63 41 56 - 85 47 60 SG 79 63 63 73 59 38 47 48 69 - 87 45 SN 63 88 53 55 63 38 41 74 31 85 - 55 VP 63 63 81 59 56 50 47 70 46 52 27 - Table 3. Pairwise cross-validated reclassification (%) among the different climatic regions of India. Values below the diagonal correspond to the proportion of Group 1 mosquito specimens correctly identified after comparison with Group 2. Values above the diagonal correspond to the proportion of Group 2 mosquito specimens correctly identified after comparison with Group 1. ( p -value < 0.0001). (Abbreviation: ARD – Arid, HST – Humid sub-tropical, MTN – Mountain, SAD – Sami-arid, TWD – Tropical wet and dry) Group 2 Reclassification test ARD HST MTN SAD TWD Group 1 ARD - 78 60 50 64 HST 70 - 60 50 60 MTN 68 77 - 59 73 SAD 66 72 47 - 75 TWD 65 65 34 57 - Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":809250,"visible":true,"origin":"","legend":"\u003cp\u003eSampling locations of \u003cem\u003eAe. aegypti\u003c/em\u003e from different climatic regions of India.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/cc4b896dbc2a7081aa1aa784.png"},{"id":96250624,"identity":"5a6542c3-34d9-4c6c-8fb3-768f2508f6bc","added_by":"auto","created_at":"2025-11-19 07:38:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":508939,"visible":true,"origin":"","legend":"\u003cp\u003ea) Position of 18 landmarks (digitized from 1st to 18th) on a right wing of female \u003cem\u003eAedes aegypti\u003c/em\u003e. b) Landmark positions derived from Procrustes superimposition.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/38bcfbdc9785e3c59b58cbc6.png"},{"id":96174697,"identity":"4f1fdd69-0838-490a-b172-7828d80bc0f9","added_by":"auto","created_at":"2025-11-18 11:18:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89197,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in wing size of \u003cem\u003eAe. aegypti\u003c/em\u003e across Indian populations (a) and climatic regions (b).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/cba1782c1779d8b94ed7aa2d.png"},{"id":96174706,"identity":"0fcd9f01-eb4d-42b2-bf66-8852241ea0fa","added_by":"auto","created_at":"2025-11-18 11:18:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":231663,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in wing shape of \u003cem\u003eAe. aegypti\u003c/em\u003e across Indian populations (a) and climatic regions (b).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/84efbcf7840da8e980657527.png"},{"id":96174700,"identity":"2ae6089e-c854-4d7d-b5fc-9e3601595fad","added_by":"auto","created_at":"2025-11-18 11:18:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":97017,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic relationships based on Mahalanobis distances among \u003cem\u003eAe. aegypti\u003c/em\u003epopulations (a) and climatic regions (b).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/687e5e4a338be10258bbec47.png"},{"id":96249525,"identity":"235b6511-c9a1-4ad8-b2b0-f32ad5c65905","added_by":"auto","created_at":"2025-11-19 07:33:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":180615,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) biplots showing associations between environmental variables and wing size variation in \u003cem\u003eAe. aegypti\u003c/em\u003e: (a) by population, (b) by climatic region.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/c3e2046072efc9792e8f4376.png"},{"id":96257206,"identity":"2f4eab7d-696b-42a7-8ac6-89c41aca10b6","added_by":"auto","created_at":"2025-11-19 07:51:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2853874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/eecbfeae-bdbe-4bfe-a938-b7979c03b709.pdf"},{"id":96174698,"identity":"26e07405-4149-4662-ba51-a3210e04eb19","added_by":"auto","created_at":"2025-11-18 11:18:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18081,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/c22eed6927a538d66385ff41.docx"},{"id":96174702,"identity":"d6caf8d6-087b-4182-a966-330d3baafe5b","added_by":"auto","created_at":"2025-11-18 11:18:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":89303,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentaryFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7937635/v1/5f5092902bfbcde610ba0cc6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of regional climatic conditions on the wing morphometrics of the dengue vector Aedes aegypti (Diptera: Culicidae) in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDengue fever, the most prevalent mosquito-borne illness, has witnessed a substantial increase in reported cases, with a 10-fold surge observed from 2000 to 2019 globally (WHO 2023). In India, more than 0.2\u0026nbsp;million cases have been reported in the year 2023 (NCVBDC 2024). \u003cem\u003eAedes aegypti\u003c/em\u003e (Linnaeus, 1762), is the primary vector, responsible for this deadly disease. It is also the potential vector of several other epidemiologically important diseases including Chikungunya, and Zika in India (NCVBDC 2024).\u003c/p\u003e\u003cp\u003eHistorically, \u003cem\u003eAe. aegypti\u003c/em\u003e preferred non-human hosts and inhabited tropical forests, with larvae developing in tree holes. However, as the human population expanded, \u003cem\u003eAe. aegypti\u003c/em\u003e populations underwent evolutionary changes to adapt to human habitats. It is believed that this mosquito species has invaded other parts of the world through human movements and trades from Central Africa (Tabachnick 1991). Consequently, diverse ecological and geographical conditions lead to changes in \u003cem\u003eAe. aegypti\u003c/em\u003e mosquito in context to their morphology, behaviour, and genetics (Louise et al. 2015; Sumitha et al. 2023; Sharma et al. 2023).\u003c/p\u003e\u003cp\u003eVector control is considered the most effective management strategy to prevent the expansion of vector-borne diseases, making an understanding of population dynamics important. While genetic and behavioural studies have provided valuable insights into \u003cem\u003eAe. aegypti\u003c/em\u003e population dynamics and vector potential (Barrera et al. 2011; Grech et al. 2015; De et al. 2022; Sharma et al. 2023; Sumitha et al. 2023), morphological traits serve as an integrative phenotype, reflecting the combined effects of genetic variation, developmental plasticity, and environmental pressures. This approach enables the detection of subtle morphological shifts associated with local adaptation, ecological divergence, and population structure, offering a cost-effective and evolutionarily informative tool for vector monitoring and comparative morphology (Lorenz et al. 2017).\u003c/p\u003e\u003cp\u003eMorphological traits, especially wing size and shape, are increasingly recognized as reliable bioindicators of environmental stress and population-level variation in insects (Hoffmann et al. 2002). Geometric morphometric (GM) analysis is a powerful, cost-effective tool that allows precise quantification of shape variation, revealing both inter- and intraspecific differentiation (Lorenz et al. 2017). In mosquitoes, wing morphology is directly linked to flight performance, host-seeking ability, dispersal potential, and susceptibility to viral infection, collectively influencing vector competence and transmission dynamics (Scott et al. 2000; Alto et al. 2008; Yeap et al. 2013).\u003c/p\u003e\u003cp\u003eGeometric morphometric approaches have been widely applied to study morphological variation influenced by sex (Christe et al. 2016), geography (Hounkanrin et al. 2023), phylogeny (Lorenz et al. 2015), ecological associations (Carreira et al. 2011), temperature (Aytekin et al. 2009), demographic factors (Wilk-da-Silva et al. 2018), altitude (Leyton Ramos et al. 2020) and landscape structure (Hounkanrin et al. 2023). These studies consistently demonstrate that wing morphometrics serve as a non-invasive, high-resolution marker of local adaptation and environmental effects in \u003cem\u003eAedes\u003c/em\u003e species.\u003c/p\u003e\u003cp\u003eAlthough most of the Indian cities experience a tropical to subtropical climate, there exists a local variation in topography, and land use contributing to micro-climatic heterogeneity that influences \u003cem\u003eAe. aegypti\u003c/em\u003e abundance and dengue epidemiology (Sarma et al. 2022). Due to this environmental heterogeneity in India, five different geo-climatic regions can be identified: Arid, Semi-arid, Tropical wet and dry, Humid sub-tropical, and Mountain (Beck et al. 2018). It is evident that \u003cem\u003eAe. aegypti\u003c/em\u003e have invaded all these climatic regions, with documented genetic (Sumitha et al. 2023) and behavioral differences (Sharma et al. 2023) across regions. Despite some genomic and ecological studies, morphometric variation among Indian populations concerning different climatic conditions remains largely unexplored. Given the evolutionary and ecological significance of wing morphology in \u003cem\u003eAe. aegypti\u003c/em\u003e, a comparative morphometric analysis across ecological gradients can provide valuable insight into how local environmental factors shape phenotypic traits in vector populations.\u003c/p\u003e\u003cp\u003eTherefore, this study investigates the wing morphometrics of \u003cem\u003eAe. aegypti\u003c/em\u003e collected from 12 distinct locations across five climatic regions in India. By applying geometric morphometric methods, we aim to evaluate the extent of morphological variation among these populations and assess whether wing traits reflect ecological divergence and potential adaptive differentiation. This work offers a morpho-ecological perspective on vector variability and contributes to understanding the functional morphology of disease vectors in changing environments.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy sites and sample populations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAedes aegypti\u003c/em\u003e specimens were collected between November 2022 and November 2024 from 12 geographically distinct locations across India i.e., Jodhpur, Kota, Sri Ganganagar, Srinagar, Hoshiarpur, Lucknow, Nagpur, Raipur, Kolkata, Visakhapatnam, Guwahati, and Itanagar\u0026nbsp;\u0026mdash;representing five distinct climatic regions: Tropical wet and dry, Arid, Semi-arid, Humid sub-tropical and Mountain (Fig. 1). Sampling was conducted irrespective of seasons over the two-year period due to logistical constraints and the availability of a single insect collector. Detailed information on sampling sites for each population is provided in Supp. File 1. Mosquitoes were sampled within human dwellings and their surroundings in the localities of each site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample Collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eField surveys were conducted at up to 20 distinct sites in each population, maintaining a minimum distance of 500 meters between sites to minimize the likelihood of sampling closely related individuals. Previous studies indicate that, \u003cem\u003eAe. aegypti\u003c/em\u003e dispersal is typically limited, with a meta-analysis estimating a weighted mean distance travelled of approximately 106 m (Moore and Brown 2022). However, mark\u0026ndash;release\u0026ndash;recapture experiments have shown that ovipositing females may travel much farther\u0026mdash;up to 840 m in urban environments\u0026mdash;when searching for multiple oviposition sites (Reiter et al. 1995). Considering this evidence, a minimum separation of 500 m between sampling sites was adopted as a practical compromise to reduce the probability of pseudo replication. Pupae stages of \u003cem\u003eAe. aegypti\u0026nbsp;\u003c/em\u003ewas collected from approximately 2 to 17 of these sites per population, depending on the availability of breeding habitats, positivity rates, and accessibility. At each positive site, pupae were collected using a glass pipette from multiple larval habitats, primarily cement tanks and discarded tires. To reduce the likelihood of sampling siblings, only one individual was used from each distinct breeding habitat. All collected specimens were transported to the laboratory at ICMR\u0026ndash;National Institute for Research in Environmental Health (ICMR-NIREH), Bhopal, Madhya Pradesh, and reared\u0026nbsp;to adulthood under standardized laboratory conditions (temperature: 27 \u0026plusmn; 2 \u0026deg;C, relative humidity: 75 \u0026plusmn; 5%, and photoperiod: 12:12 h light: dark). Emerged adults were anesthetized using cotton soaked in 0.5ml of chloroform and identified morphologically using standard taxonomic keys (Tyagi et al. 2015). All adult specimens were stored under 4 \u0026ordm;C for morphometric study. To avoid sampling siblings, only one emerged female mosquito per developmental habitat was used for wing morphometric analysis. No adult mosquitoes were collected directly from the field during this study. A total of 256 adult female mosquitoes were included in the final wing morphometric analysis, averaging approximately 21 individuals per population. Previous studies in mosquito morphometrics have also used similar or smaller sample sizes per population to effectively capture variation in wing shape and size (Garz\u0026oacute;n and Schweigmann 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWing preparation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe right wings of female \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes were removed from the thorax and mounted on a microscope slide (15 mm x 15 mm) with a coverslip. Each wing was then photographed under 40x magnification using ImageView software (version x64, 4.11.18012.20201123) with STEMI 305 stereomicroscope.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLandmark digitisation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 18 landmarks were digitized using ImageJ ver. 1.54d (Schneider et al. 2012) software following methods described by Hounkanrin et al. (2023). The wing picture and digitization of landmarks were done by one author (GS) and repeated three times to minimize error. Total 256 wing specimens of female \u003cem\u003eAe. aegypti\u003c/em\u003e (Jodhpur - 22, Kota - 8, Sri Ganganagar - 27, Srinagar - 15, Hoshiarpur - 24, Lucknow \u0026ndash; 17, Nagpur - 27, Raipur - 13, Kolkata - 27, Visakhapatnam - 20, Guwahati - 24, and Itanagar \u0026ndash; 32) were used. The representation of 18 landmarks and their coordinates are provided in Fig. 2a and Supp. File 1 respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWing size and shape estimation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe centroid size (CS) representing wing size, was calculated from raw wing coordinates data. Centroid size is defined as the square root of the total squared distances measured from the centroid to each landmark, and it can be employed as a proxy for wing size (Dujardin 2008). Coordinates were imported into the MorphoJ ver. 1.08.01 (Klingenberg 2011) in Tab delimited format. These coordinates were aligned by performing Procrustes superimposition (Fig. 2b) to visualize the position of each landmark for each specimen using MorphoJ ver. 1.08.01 (Klingenberg 2011). To statistically compare the mean CS among specimens from different locations and regions, Tukey\u0026apos;s HSD test for multiple comparisons of means was performed using GraphPad Prism version 8.0.\u003c/p\u003e\n\u003cp\u003eThe assessment of wing shape involved the importation of wing coordinates in a text-delimited format using MorphoJ ver. 1.08.01 (Klingenberg 2011). To conduct a comparative analysis, Procrustes ANOVA was performed using the MorphoJ ver. 1.08.01. Following this, Canonical Variate Analysis (CVA) (70% confidence) was applied to visually represent shape variations based on Procrustes coordinates. Additionally, Mahalanobis distances were computed using MorphoJ ver. 1.08.01 software to further quantify the differences in wing shape.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure the species confirmation, all the specimens were analysed morphologically as well as at the molecular level following the method described by Higa et al. (2010) targeting the ITS region of the genus Aedes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhenetic relationship\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeighbour-Joining (NJ) tree was constructed with 100 bootstrap replicates based on Mahalanobis distances obtained through pairwise comparison of wing specimens via CVA using PAST software v.4.03 (Hammer et al. 2001) to illustrate the phenetic relationships among the populations and, among the climatic regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAllometric effect\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the influence of wing size on wing shape (allometry), the multivariate regression of the Procrustes coordinates (dependent variable) against log CS (independent variable) was analysed using a permutation test with 10,000 randomizations using MorphoJ ver. 1.08.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffect of environmental factors on wing size\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) was employed at both the population level and the climatic region level to elucidate the influence of environmental variables on wing centroid size variation in \u003cem\u003eAe. aegypti\u003c/em\u003e across India. The environmental variables include the mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), relative humidity (RH), precipitation, latitude, and various measures of soil moisture (surface at 5 cm, root zone at 100 cm and profile depth) (Supp. Table S1). These environmental parameters for all the sampled locations were retrieved from the National Aeronautics and Space Administration (NASA) Langley Research Centre Prediction of Worldwide Energy Resource Project (https://power.larc.nasa.gov/data-access-viewer/ ).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe present study conducted wing morphometric analyses using a total of 256 wing specimens obtained from 12 distinct populations of \u003cem\u003eAe. aegypti\u003c/em\u003e (Fig. 1 and Supp. File 1) in India. The mean CS estimated from the coordinates of 18 wing landmarks, exhibited a significant difference among the populations (\u003cem\u003eF\u003c/em\u003e ratio = 236.3, \u003cem\u003ep\u003c/em\u003e = \u0026lt;0.0001) as well as among the climatic regions (\u003cem\u003eF\u003c/em\u003e ratio = 22.29, \u003cem\u003ep\u003c/em\u003e = \u0026lt;0.0001). The Nagpur population showed the highest CS (4.49 \u0026plusmn; 0.31 mm), while the Srinagar population presented the lowest CS (1.93 \u0026plusmn; 0.11 mm) (Fig. 3a, Table 1). Similarly, the Arid region showed the highest CS (3.95 \u0026plusmn; 0.56 mm) while the Mountain region displayed the lowest CS (1.93 \u0026plusmn; 0.11 mm) (Fig. 3b, Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCanonical Variate Analysis (CVA), validated through Procrustes ANOVA, revealed significant differences in wing shape both among populations (\u003cem\u003eF\u003c/em\u003e = 2.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) and among climatic regions (\u003cem\u003eF\u003c/em\u003e = 1.75, \u003cem\u003ep\u003c/em\u003e = 0.01). At the population level, CVA accounted for a total of 36.31% of the shape variance, with CV1 explaining 20.94% and CV2 contributing 15.37% of the variation (Fig. 4a). Most populations exhibited varying degrees of overlap, indicating shared morphological traits. However, the Kota (KT) population was distinctly separated along CV1, suggesting a significantly divergent wing morphology relative to other populations. At the climatic region level, the analysis explained a higher proportion of the total variation (72.81%), with CV1 accounting for 41.51% and CV2 for 31.29% (Fig. 4b). Although considerable overlap was observed among the regions as well as populations. The Semi-Arid (SAD) region and Kota (KT) population exhibited the widest dispersion, indicating high variability in wing morphology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCross-validated pairwise reclassification based on population-level comparisons demonstrated a high degree of wing shape differentiation (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), with classification accuracies ranging from low (VP vs. SN) to high (JD vs. GW; SN vs. HP). Approximately 79% of population-level comparisons exceeded the 50% accuracy threshold (Table 2). Similarly, reclassification analysis across climatic regions (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) revealed that 90% of pairwise comparisons surpassed the 50% accuracy threshold, with classification accuracies ranging from low (TWD vs. MTN) to high (HST vs. ARD) (Table 3). These results provide strong statistical evidence for wing shape divergence both among the population and among the climatic regions.\u003c/p\u003e\n\u003cp\u003eThe Neighbor-Joining (NJ) trees constructed using Mahalanobis distances derived from canonical variate analysis (CVA) effectively illustrated the phenetic relationships among \u003cem\u003eAe. aegypti\u003c/em\u003e populations (Fig. 5a) and among different climatic regions of India (Fig. 5b). At the population level, two major clusters were observed: one comprising KT, LK, VP, RP, HP, and JD, and another including GW, IT, KK, SG, SN, and NG. The subgrouping of KT and LK showed the strongest bootstrap support (91%), followed by VP-RP (36%) and HP-JD (48%). Within the second cluster, distinct pairings were observed between GW and IT (55%) and KK and SG (27%), with SN and NG forming the terminal branch of this group. Similarly, at the climatic region level, two primary clusters were evident. One comprises HST, TWD, and ARD regions while the other includes MTN and SAD regions. Within the first cluster, HST and TWD formed a well-supported subgroup (bootstrap support: 82%), followed by the inclusion of ARD. The second cluster grouped MTN and SAD with moderate bootstrap support (49%).\u003c/p\u003e\n\u003cp\u003eMultivariate regression of the Procrustes coordinates on log CS showed an allometric effect of wing size on wing shape. (Population: 5.89%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; Region: 2.40%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). We excluded this effect from the subsequent analysis.\u003c/p\u003e\n\u003cp\u003eRedundancy Analysis (RDA) was conducted to examine the influence of environmental variables on the mean wing centroid size (mean CS) of \u003cem\u003eAe. aegypti\u003c/em\u003e at both population (Fig. 6a) and climatic region levels (Fig. 6b). At the population level, the first two principal components (PCA1: 82.1%, PCA2: 17.9%) accounted for the total variance. Mean centroid size showed strong positive associations with root soil moisture (100 cm), diurnal temperature range (DTR), and latitude (N). Profile soil moisture (StoB) exhibited a moderate negative influence on mean CS, along with precipitation (mm/day) and surface soil moisture (5 cm), which projected in opposite directions. In contrast, at the climatic region level, PCA2 alone explained most of the variance (99.8%). Mean CS aligned distinctly along PCA1, while most environmental variables including precipitation, and soil moisture at multiple depths (5 cm, 100 cm, StoB), showed strong projections along PCA2. Notably, precipitation and surface soil moisture exhibited a strong inverse association with wing size, indicating that mosquitoes from wetter regions tend to have smaller wings, as observed in the Mountain region and Srinagar population.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eAedes aegypti\u003c/em\u003e exhibits a remarkable adaptability to anthropogenic modifications, thriving across a diverse range of environmental conditions. To thrive in such diverse environmental settings, \u003cem\u003eAe. aegypti\u003c/em\u003e must overcome selective pressures. Previous research has highlighted the varied climatic conditions in India (Beck et al. 2018) and their impacts on mosquitoes, influencing their genetics (Sumitha et al. 2023), physiology (Sharma et al. 2023), and morphology (Hounkanrin et al. 2023).\u003c/p\u003e\u003cp\u003eExpanding the existing knowledge, the present study analysed the wing morphometrics of 12 \u003cem\u003eAe. aegypti\u003c/em\u003e populations from five different climatic regions of India to understand the influence of native environmental conditions on wing size and shape. The climatic regions included Arid (Jodhpur, Sri Ganganagar), Semi-arid (Kota, Hoshiarpur), Tropical wet and dry (Nagpur, Raipur, Visakhapatnam, Kolkata), Mountain (Srinagar), and Humid sub-tropical (Lucknow, Guwahati, Itanagar). This regional classification reflects the broad spectrum of ecological conditions that shape \u003cem\u003eAe. aegypti\u003c/em\u003e populations throughout India.\u003c/p\u003e\u003cp\u003eWing size is commonly used as a proxy for overall mosquito size (Dujardin 2008), and it can influence various physiological and behavioural aspects of mosquitoes (Yeap et al. 2013). Larger mosquitoes, characterized by greater wing size, often exhibit distinct metabolic profiles, enhanced blood-feeding capacities, and improved flight capabilities compared to their smaller-size mosquitoes (Yeap et al. 2013). These factors can collectively affect the mosquito's ability to transmit diseases. For example, larger mosquitoes are less susceptible to viral infections, potentially influencing the dynamics of virus transmission within mosquito populations (Alto et al. 2008). Therefore, wing morphometric studies have practical implications for formulating more effective vector management strategies in specific areas, emphasizing the importance of considering local environmental conditions in such interventions. The present study found that mosquitoes from arid regions (Jodhpur and Sri Ganganagar) exhibited larger wing sizes (3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 mm) among all regions. This phenomenon can be attributed to the effects of high temperatures, which accelerate larval metabolic rates and increase mortality during development. Consequently, the surviving larvae face reduced competition and have access to greater nutritional resources, promoting larger body and wing sizes. These observations align with the findings of Mohammed et al. (2011), who demonstrated that \u003cem\u003eAe. aegypti\u003c/em\u003e larvae reared under higher, fluctuating temperatures (25 \u0026ordm;C-35 \u0026ordm;C) in Trinidad, West Indies, developed into larger adults with bigger wings. Conversely, mosquitoes from the mountain region (Srinagar) showed shorter wing size (1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 mm). This can be explained as lower temperature conditions that prevail in mountainous areas, slow down the development rate and results in prolonged exposure to suboptimal conditions, which can limit growth. Additionally, colder temperatures can reduce metabolic efficiency in mosquito larvae, affecting their ability to assimilate and utilize nutrients effectively. These trends are consistent with previous studies indicating that low temperatures reduce metabolic performance in larvae and affect nutrient utilization (Carrington et al. 2013).\u003c/p\u003e\u003cp\u003eMoreover, while temperature appears to be a key driver of wing size variation, other environmental factors such as humidity, soil moisture, precipitation, larval density, food availability, and urbanization may also contribute to the observed differences (Alto and Juliano 2001; Pautzke et al. 2024; Jirakanjanakit et al. 2007; Oliveira-Christe et al. 2023; Hounkanrin et al. 2023). Mohammed et al. (2011) similarly reported no significant linear correlation between centroid size and temperature, reinforcing the idea that wing size is shaped by multifactorial influences. This aspect was further supported by the RDA analysis in the present study which shows mean CS was positively affected by root soil moisture (100 cm), diurnal temperature range (DTR), and latitude (N) at the population level. Whereas, variables such as profile soil moisture (StoB), precipitation (mm/day), and surface soil moisture (5 cm) were negatively associated with wing size at the population level. This indicates that higher DTR and deeper soil moisture may support greater mosquito growth (Lockaby et al. 2016). Similarly, precipitation and surface soil moisture showed an inverse effect on mean CS at the climatic region level, which further suggests that mosquitoes from wetter regions tend to have smaller wings. These findings collectively reinforce that no single factor, such as temperature, entirely determines wing size; rather, it is the complex interplay of multiple ecological variables that shape morphometric outcomes.\u003c/p\u003e\u003cp\u003eAlthough wing size offers valuable insights, it is recognized as being more vulnerable to environmental variations (Lorenz et al. 2017). For example, in \u003cem\u003eAe. albopictus\u003c/em\u003e populations from Thailand, wing size was found to be influenced by climatic conditions (Vargas et al. 2010). This susceptibility necessitates caution when interpreting wing size data, as it may reflect short-term environmental effects rather than stable population-level traits. In contrast, wing shape is more robust against such environmental fluctuations and is considered a better indicator of heritable, intraspecific, and regional differences (Carvajal et al. 2016; Rodr\u0026iacute;guez-Zabala et al. 2016; Krtinić et al. 2016). In the present study, the morphospace analysis, using Canonical Variate Analysis (CVA), reveals statistically significant differences in wing shape among the populations as well as across climatic regions. Notably, the Semi-Arid region (Kota population) occupied a larger morphospace than the other regions, indicating a higher degree of shape variation. This distinctiveness suggests a unique environmental influence or possible local adaptation. In contrast, several other populations and regions exhibited overlapping morphospace, reflecting similarity in wing shapes likely driven by shared environmental conditions. These morphological distinctions were further supported by a cross-validated reclassification test, which demonstrated that 79% of pairwise population comparisons and 90% of region-wise comparisons had classification accuracies above 50%. Such accuracy levels highlight the reliability of wing shape as a marker for population differentiation and suggest climatic variability plays a significant role in shaping population structure in \u003cem\u003eAe. aegypti\u003c/em\u003e. Interestingly, comparable patterns have also been reported in other parts of the world. A study from Vila Toninho, Brazil (Prado et al. 2022), reported significant seasonal and spatial variation in the wing shape of \u003cem\u003eAe. aegypti\u003c/em\u003e females. The most pronounced differences occurred between winter and spring, as well as among sites during the summer, corresponding with fluctuations in dengue transmission intensity. These changes in wing shape were interpreted as signs of microevolution, likely driven by localized environmental pressures such as temperature shifts and precipitation variability. Taken together, these findings reinforce the idea that wing shape variation in \u003cem\u003eAe. aegypti\u003c/em\u003e reflects underlying ecological and evolutionary processes acting at both regional and local scales.\u003c/p\u003e\u003cp\u003eThe Neighbor-Joining (NJ) trees derived from Mahalanobis distances revealed clear patterns in the phenetic relationships among \u003cem\u003eAe. aegypti\u003c/em\u003e populations and their corresponding climatic regions. At the population level, two primary clusters emerged: the first included KT, LK, VP, RP, HP, and JD, while the second comprised GW, IT, KK, SG, SN, and NG. Interestingly, populations from climatically distinct regions such as JD (Arid) and HP (Semi-Arid), and RP and VP (TWD), clustered together in the first group. These associations suggest that morphological similarity is not strictly governed by macroclimatic classification but may instead be influenced by shared micro-environmental conditions, such as urbanization level, breeding habitat types, and anthropogenic disturbances. Similarly, the region-level NJ tree revealed two major clades: one grouping HST, TWD, and ARD regions, and the other clustering MTN and SAD. The inclusion of ARD with more humid regions (HST and TWD) indicates convergence driven by comparable ecological pressures in urban environments, such as intermittent water availability, artificial containers, and temperature fluctuations. Together, the population- and region-level trees underscore that wing shape variation in \u003cem\u003eAe. aegypti\u003c/em\u003e is shaped by a complex interplay of local environmental and anthropogenic factors, rather than climatic region alone. However, while these clustering patterns provide ecologically plausible interpretations, their reliability warrants caution, as several groupings were supported by low to moderate bootstrap values, indicating limited statistical robustness. Further genetic studies may shed more light on this regard.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study highlights significant morphological variation in \u003cem\u003eAedes aegypti\u003c/em\u003e across 12 geographically diverse populations and five major climatic regions of India. Notable differences in wing size and shape were observed, with the largest wings recorded in the Nagpur population and the smallest in Srinagar. Regionally, mosquitoes from the Arid region exhibited larger wings, while those from the Mountain region had smaller ones. However, these patterns were not solely attributable to temperature. Instead, the observed variation appears to result from a multifactorial interplay of environmental conditions. The pronounced wing shape divergence, particularly in the Semi-Arid region (Kota), further supports the role of local adaptation. Overall, this study demonstrates that geometric morphometric analysis is a valuable tool for detecting intraspecific variation and understanding ecological influences on mosquito populations, offering insights that can enhance vector surveillance and control strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDerived data supporting the findings of this study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr. Surya Singh, Scientist-B, ICMR-National Institute for Research in Environmental Health, Bhopal, India, for providing technical support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGS: Conceptualization, Data Curation, Resources, Software, Validation, Writing \u0026ndash; original draft. RB: Supervision, Resources, Software, Writing - review \u0026amp; editing. DKS: Conceptualization, Supervision, Funding acquisition, Project administration, Writing \u0026ndash;original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Indian Council of Medical Research under the scheme ICMR-SRF (No. Fellowship/106/2022-ECD-II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study does not require any ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlto BW, Juliano SA (2001) Precipitation and temperature effects on populations of Aedes albopictus (Diptera: Culicidae): implications for range expansion. 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Accessed on 21 December 2023\u003c/li\u003e\n\u003cli\u003eYeap HL, Endersby NM, Johnson PH, et al. (2013) Body size and wing shape measurements as quality indicators of Aedes aegypti mosquitoes destined for field release. Am J Trop Med Hyg 89:78. https://doi.org/10.4269/ajtmh.12-0719\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003ePopulation and climatic region wise mean centroid sizes (CS) of \u003cem\u003eAedes aegypti\u003c/em\u003e in India. Bold numbers represent the mean centroid size (CS) and the total number of \u003cem\u003eAedes aegypti\u003c/em\u003e mosquitoes for each climatic region.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation \u0026amp; climatic regions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude (E)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean CS\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (no of wing analyzed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eVisakhapatnam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e17.6868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e83.2185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.09 \u0026plusmn; 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eKolkata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e22.5744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e88.3629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.00 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eNagpur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e21.1458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e79.0882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.49 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eRaipur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e21.2514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e81.6296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.41 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTropical Wet and Dry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.61 \u0026plusmn; 1.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eJodhpur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e26.2389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e73.0243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.55 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eSri Ganganagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e29.9094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e73.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.29 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.95 \u0026plusmn; 0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eKota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e25.2138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e75.8648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.71 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eHoshiarpur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e31.5143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e75.9115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.32 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSemi-Arid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.67 \u0026plusmn; 0.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eItanagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e27.0844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e93.6053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.09 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eGuwahati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e26.1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e91.7086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.38 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eLucknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e26.8467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e80.9462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.58 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHumid Sub tropical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.96 \u0026plusmn; 1.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003eSrinagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e34.0837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e74.7973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.93 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMountain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.93 \u0026plusmn; 0.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Pairwise cross-validated reclassification (%) among the different populations of India. Values below the diagonal correspond to the proportion of Group 1 mosquito specimens correctly identified after comparison with Group 2. Values above the diagonal correspond to the proportion of Group 2 mosquito specimens correctly identified after comparison with Group 1. (\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.0001). \u0026nbsp;(Abbreviation: GW - Guwahati, HP - Hoshiarpur, IT - Itanagar, JD - Jodhpur, KK - Kolkata, KT - Kota, LK - Lucknow, NG - Nagpur, RP - Raipur, SG \u0026ndash; Sri Ganganagar, SN - Srinagar, VP - Visakhapatnam).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReclassification test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Pairwise cross-validated reclassification (%) among the different climatic regions of India. Values below the diagonal correspond to the proportion of Group 1 mosquito specimens correctly identified after comparison with Group 2. Values above the diagonal correspond to the proportion of Group 2 mosquito specimens correctly identified after comparison with Group 1. (\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.0001). (Abbreviation: ARD \u0026ndash; Arid, HST \u0026ndash; Humid sub-tropical, MTN \u0026ndash; Mountain, SAD \u0026ndash; Sami-arid, TWD \u0026ndash; Tropical wet and dry)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eReclassification test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eARD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eARD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTWD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aedes aegypti, dengue, wing morphometrics, size, shape, climate region","lastPublishedDoi":"10.21203/rs.3.rs-7937635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7937635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eAedes aegypti\u003c/em\u003e (Linnaeus, 1762), a major arboviral vector of global importance, demonstrates high adaptability across diverse environments. In India, \u003cem\u003eAe. aegypti\u003c/em\u003e is widespread across the country, thriving in highly diverse environmental conditions. Given its genetic, behavioral, and physiological variability, this study explores whether environmental factors also influence phenotypic traits such as wing morphology across the diverse climatic conditions of India. Right wings from 256 female \u003cem\u003eAe. aegypti\u003c/em\u003e specimens across 12 populations in five major climatic regions of India, \u003cem\u003eviz.\u003c/em\u003e, Arid, Semi-Arid, Tropical Wet, and Dry, Mountain, and Humid Subtropical, were analyzed for morphometric variation. Significant differences in wing centroid size (CS) and shape were observed among both the populations and climatic regions. Arid region (3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56) and Nagpur population (4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31) exhibited the largest wings, while Mountain region (Srinagar population) showed the smallest (1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11). Canonical Variate Analysis (CVA) revealed significant wing shape differences among both populations and regions, with the Semi-Arid region and Kota population showing distinct divergence. Cross-validated reclassification demonstrated high accuracy, with 79% of population-level and 90% of region-level comparisons exceeding 50%. Redundancy Analysis (RDA) showed that wing size was positively influenced by diurnal temperature range, root-soil moisture, and latitude, while precipitation and surface moisture had negative effects. Neighbor-Joining trees highlighted phenetic clustering influenced more by local environmental conditions than macroclimatic zones. Conclusively, this study highlights the role of environmental variation in shaping wing morphometry, providing insights into population differentiation and aiding region-specific vector control strategies.\u003c/p\u003e","manuscriptTitle":"Effects of regional climatic conditions on the wing morphometrics of the dengue vector Aedes aegypti (Diptera: Culicidae) in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 11:18:32","doi":"10.21203/rs.3.rs-7937635/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T05:20:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T12:41:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245708746857694902781558827740828696074","date":"2026-03-11T10:18:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T16:03:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T12:51:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T12:49:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biologia","date":"2025-10-24T06:58:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1b567c42-72b4-475d-a10b-626daf9c85ef","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T05:20:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T07:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 11:18:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7937635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7937635","identity":"rs-7937635","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}