Impact of Climate Change on Culex pipiens Mosquito Distribution in the United States

Abstract

Culex pipiens (Diptera: Culicidae) is a disease vector for the West Nile virus (WNV). Climate change impacts the disease transmission carried by mosquitoes, and it is partly to blame for the resurgence of mosquitoes as important disease vector insects. This study assessed the geographic range of Culex pipiens in the United States under both present and projected climatic scenarios, identifying the primary environmental factors influencing its distribution. Employing species distribution modeling (MaxEnt, Version 3.4.1) and occurrence data from the Global Biodiversity Information Facility, we examined the effects of key variables, including altitude, temperature seasonality (bio4), and annual precipitation (bio12), on habitat suitability for C. pipiens. The analysis revealed that altitude accounted for 60.3% of the model’s explanatory power, followed by temperature seasonality (31%) and annual precipitation (8.7%). Areas having low elevation and moderate temperature fluctuations were the most favorable for C. pipiens, with a predicted range extending across the Midwest and southeastern United States under present variables. Future projections for 2050 and 2070 under Representative Concentration Pathway (climatic change) scenarios suggest possible northward expansion in response to rising temperatures and altered precipitation patterns. The study highlights some shifts in C. pipiens distribution and the potential for increased disease transmission into new areas. This study serves as a catalyst for decision-makers to coordinate their management reaction and create more resilient and comprehensive strategies to safeguard human health.

1. Introduction

Vector-borne diseases (VBDs) are primarily transmitted by arthropod vectors, such as mosquitoes. These diseases are particularly sensitive to variations in external climatic conditions due to the ectothermic (cold-blooded) nature of the vectors, which rely on ambient temperature to regulate their physiological processes [1]. The appropriateness of the habitat affects the number, distribution, and quantity of insects. Additionally, temperature affects how quickly viruses develop and multiply in mosquitoes, raising the possibility of human transmission [2,3]. Mosquito vectors, which undergo an aquatic developmental stage and can transmit diseases through various mechanisms, are significantly influenced by variations in precipitation due to differences in their ecological requirements. Precipitation directly affects the availability of breeding sites and, consequently, the abundance and distribution of these vectors [4]. Mosquito-borne diseases (MBDs) have been on the rise as a consequence of climate change. In Canada, the incidence of MBDs has increased by 10% over the past two decades, largely attributed to shifts in climatic conditions. Similarly, in the United States, reported cases of vector-borne diseases (VBDs) have more than doubled during the same period [5,6]. This is because land use, temperature, and precipitation all affect mosquito life cycles, reproduction, and feeding [7]. Climate change may alter the distribution, habitat, and seasonality of disease-carrying mosquitoes. Many ecosystems around the world face this difficulty since biotic biodiversity, including insects, is impacted by impaired ecological processes [8]. Therefore, one of the most significant variables linked to the spread of insect pests is climate change. West Nile virus (WNV), lymphatic filariasis (LF), Rift Valley fever virus (RVFV), Saint Louis encephalitis virus (SLEV), and Sindbis virus are among the major etiological agents of human and animal diseases that can be spread by Culex pipiens (Diptera: Culicidae), a mosquito species complex [9,10,11,12]. Although most WNV infections in humans are asymptomatic, 20% of cases cause a feverish illness known as West Nile fever (WNF), and 1% of cases (in older or immunocompromised individuals) result in a severe or occasionally fatal neurological disease known as West Nile virus neuroinvasive disease (WNND) [13]. According to data from the US CDC between 1999 and 2021, WNV was responsible for 2600 deaths and more than 55,000 recorded instances of human disease, with over 27,000 of those cases being neuroinvasive [14]. Because of the health problems transmitted by mosquitoes, there have been attempts to combat them [15,16,17,18,19]. Due to changes in global temperatures, many mosquitoes that are significant for public health, such as Culex species, will spread to new areas [19]. Studies looking at the future patterns of other MBDs, such dengue and malaria, have projected that climate change will cause these diseases to spread geographically and transmit more intensely [19,20]. A growing body of evidence indicates that certain species’ range distributions have already begun to vary due to shifting climatic circumstances, and that this trend is probably going to continue as climate change continues [21,22]. While biotic factors such as competition, predation, and vector control measures play a significant role in influencing mosquito abundance at local scales, environmental abiotic factors—such as climate and topography—typically exert a stronger influence at broader regional scales [23]. Ecological niche models (ENM) and bioclimatic envelope models (BEM) have gained popularity as ways to model potential impacts of climate change on species distributions [24,25]. Environmental factors have been implicated in significant variations in the characteristics of immature and adult insects, including fertility, longevity, body size, development timelines, and larval growth rates [26,27]. Temperature is a particularly important abiotic factor for mosquitoes and other arthropods because it directly affects mortality, life duration, and development rates, all of which can lead to changes in morphology [28,29,30]. Species distribution models (SDMs), which have received increased attention in conservation and biogeographical studies, are the most widely used scientific technique for determining potential climate change implications on biodiversity [31]. These models have been extensively and routinely employed to evaluate the ecological and evolutionary dynamics that influence the global distribution of species, as well as the suitability of their habitats [32,33]. SDMs are used in many ecological, biological, and biogeographical applications to predict the historical, current, and future distribution of species [34]. Climate conditions have often been highlighted as the main factors influencing the regional distribution of biodiversity globally [35].

The objectives of this research include

• Evaluating C. pipiens present distribution in the US while taking ecological and climatic elements into account.
• Looking into how climate change can affect C. pipiens habitat suitability, paying special attention to how temperatures are rising and how precipitation patterns are changing.
• Analyzing the variations between scenarios and forecasting the future distribution range of C. pipiens in 2050 and 2070 under various climate impacts.
• Examining the main ecological and climatic parameters that contribute to the invasiveness or endemic patterns of C. pipiens and their capacity to change the dynamics of disease transmission.

2. Materials and Methods

2.1. Global Spatial Information

Spatial data for C. pipiens were obtained from Global Biodiversity Information Facility (GBIF.org, https://doi.org/10.15468/dl.sgpgg0, accessed on 7 April 2022). 161,763 geo-referenced records with locations in the downloaded database were derived from preserved specimens and human observations from July 1896 to April 2023. After removing repeated geographic data and stations outside the the map boundaries, the downloaded database was geo-referenced using ArcGIS 10.3. The occurrence data were derived from both observation of human and stored samples. We used ArcGIS 10.3 [36] to validate the records and remove duplicate geographical records and records that were placed in water or outside the shapefile [36] of the USA map. After the matching missing values for the topography and climate resampled environmental variables were eliminated, the 1313 distribution points into 1308 records.

2.2. Environmental Variables and Multicollinearity

Twenty characteristics were identified as factors to predict the ecological niche of C. pipiens depend on the existing presence information. Elevation and ninteen variable were included in the WorldClim database (http://www.worldclim.org/, accessed on 21 March 2020) at a resolution of 2.5 arcminutes (~5 km × 5 km at the equator) [37]. Previous studies had demonstrated that these environmental features were the most crucial for determining the potential species’ range [38]. To assess the projected climate change impacts on the C. pipiens habitat range, we used the global general circulation models (GCMs) BCC-CSM1.1 “Beijing Climate Centre—Climate System Modelling 1.1, http://forecast.bcccsm.ncc-cma.net/web/channel-34.htm”, accessed on 21 March 2020). The global climate model BCC-CSM1 for both scenarios was sourced from the WorldClim database for (2041–2060) and (2061–2080). The significance of predictor variables in determining the potential distribution of C. pipiens was assessed by eliminating correlated factors with variance inflation factor (VIF) values exceeding 5, as well as those exhibiting a correlation greater than 0.75, to mitigate issues of multicollinearity. In the realm of statistics, the variance inflation factor (VIF) serves as a precise tool to gauge how much one predictor might be accurately captured by other predictors. This assessment involves squaring the correlations derived from comparing a predictor against all other predictors. Through the computation of VIF values, we can pinpoint the level of correlation between predictors, a crucial factor that could influence the trustworthiness of our regression outcomes [39]. VIF values surpassing 10 are viewed as troublesome, signaling significant multicollinearity and the potential for bias in regression coefficients. Careful scrutiny of VIF values for predictor variables is necessary, prompting appropriate measures like variable selection or transformation to tackle any issues stemming from multicollinearity.

2.3. MaxEnt Model

A modeling technique called MaxEnt (Maximum Entropy) is used to forecast the distribution of species and the appropriateness of their habitats. The possibility for species to locate in areas that have not been surveyed can be evaluated with the help of species distribution models (SDMs) [40,41,42,43]. These models have been used to identify key areas for conservation [34] and to give a baseline for forecasting how a species would react to changes in the landscape and/or climate change [44]. According to recent research, even with limited records, a statistical mechanics technique like the MaxEnt technique works incredibly well [45]. Illustrating the importance of predictor factors in the potential distribution of C. pipiens involved removing correlated factors with variance inflation factor (VIF) values exceeding 5 and maintaining a correlation threshold of 0.75 to mitigate issues related to multicollinearity. The Variance Inflation Factors (VIFs) of twenty environmental variables were assessed to identify and eliminate multicollinearity, thereby selecting the most influential predictors with the highest contribution to the model.We eliminated highly correlated variables based on their variance inflation factor (VIF) in order to lessen overfitting of SDM models.

Based on environmental factors and known species geographic occurrences, the MaxEnt model calculates a species’ most likely, uniform probability distribution (also known as the maximum entropy distribution). MaxEnt is predicated on the idea that, within the limitations imposed by current environmental factors, the observed species occurrences sampled by surveillance represent the most uniform distribution of the species. Based on the given environmental data, the MaxEnt optimization algorithm generates a habitat suitability map that illustrates the probability of a particular species’ occurrence over a geographic area. Using environmental factors like altitude, temperature seasonality, and annual precipitation, we used the MaxEnt model to forecast the possible geographic spread of C. pipiens in the USA under various climatic conditions. The results can help researchers and policymakers better understand how climate change and environmental factors can impact the spread of disease vectors like Culex mosquitoes. We used MaxEnt software (Version 3.4.1) available at http://biodiversityinformatics.amnh.org/open_source/maxent/ (accessed on 2 December 2020) to model the mosquito distributions and niches. Tests were conducted using 25% of the occurrence data, while training was conducted using 75% of the data. The linear, quadratic, hinge, and product were all programmed to run automatically.

The MaxEnt modeling process retained three environmental variables: ALT, bio4, and bio12. These non-linear factors—except for elevation—were subsequently used to predict the distribution of C. pipiens under future global warming scenarios. To mitigate multicollinearity and select the most influential predictors with the greatest contribution to the model, the Variance Inflation Factors (VIFs) of twenty ecological factors were analyzed. To reduce the risk of overfitting in species distribution models (SDMs), strongly correlated variables were excluded based on their VIF, which quantifies the extent to which each predictor is explained by the others [46]. As suggested by [47], we used the vifcor and vifstep functions of the package “usdm” [48] in R 3.5.3 to perform VIF analysis by excluding factors with VIF values more than five and a correlation threshold of 0.75. The function of the “SDM” package in R 3.5.3 was used to estimate the relative relevance of predictor factors.

3. Results and Potential Habitat Classification

3.1. Evaluation of the Model

Utilizing various factors such as climatic and topographic variables significantly influences the evaluation of the MaxEnt model, as evidenced by statistical significance (p < 0.05) in terms of Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) results. AUC values typically range between 0 and 1, where an AUC of 1 indicates an ideal model, while a model with an AUC of 0.5 performs no better than random chance [45,49]. The model’s precision is assessed based on the Area Under the Curve (AUC) [50]. Maxent supplied the percentage contribution of each predictor to the output model.

3.2. Climatic Variables Importance

Our results indicated that three uncorrelated factors were included in the MaxEnt models (

Table 1. Permutation importance of variables for modeling. A transformation is performed on the standard deviation (multiplying by 100) to scale the temperature seasonality values for modeling without altering the underlying variability of the data.

Code	Variables	Units	Percent Contribution (%)	VIF
Alt	Altitude	m	60.3	1.26
bio_04	Temperature Seasonality (standard deviation × 100)	°C	31	2.61
bio_12	Annual precipitation (mm))	mm	8.7	2.36

). The sensitivity of C. pipiens to altitude (Alt), Temperature Seasonality (bio4, standard deviation × 100), and annual precipitation (bio12, in mm) was significant, with respective contributions of 60.3%, 31%, and 8.7%. These were discovered to significantly affect C. pipiens ability to adapt to current and future conditions. The three environmental variables that had the biggest impact on the MaxEnt model’s prediction of the distribution of C. pipiens were these bioclimatic variables. The most significant environmental factor that contributed most to the distribution of C. pipiens was altitude (Alt, 60.3%). On the other hand, the least amount was contributed by (annual precipitation (mm)) (bio12, 8.7%). The respective variable contributions in the various models are summarized in the table below (Table 1).

3.3. Spatial Prediction Model and Distribution Range of C. pipiens in the USA

Figure 1 displays the MaxEnt model for C. pipiens. The habitat of C. pipiens is expected to span large areas of the United States. The model was trained using 376 presence reports, and it was tested using 125. With a standard deviation of 0.021, the AUC (Figure 1) for the test points was 0.755 and for the training points it was 0.747. As indicated by the acceptable discrimination (0.7–0.8), all of the MaxEnt models revealed comparatively good levels of accuracy in predicting the climatic adaptability of C. pipiens. Under present and future climatic conditions, the AUC values varied from 0.733 to 0.770, respectively (

Table 2. The AUC values for the C. pipiens climatic suitability models run in MaxEnt.

Climatic Scenario	AUC Value
Current Climate	0.747
BCC-CSM1_ssp126_2041-2060	0.749
BCC-CSM1_ssp126_2061-2080	0.770
BCC-CSM1_ssp585_2041-2060	0.745
BCC-CSM1_ssp585_2061-2080	0.733

). The BCC-CSM1_ssp126_2061-2080 Scenario had the highest AUC value (0.770), while the BCC-CSM1_ssp585_2061-2080 Scenario had the lowest AUC value (0.733), according to the models’ predictive performance (Table 2).

3.4. Model Assessments

We used the Maximum Entropy model to forecast possible C. pipiens under AUC equals 0.747. The random prediction value (0.5) was substantially lower than the mean AUC range of C. pipiens. Because the prediction findings were so accurate, MaxEnt’s estimates for the potential distribution region could likewise be trusted.

Using an iterative process, MaxEnt corrects the alteration of individual evaluation variable and coefficients in the model after calculating the contribution of environmental factors to the prediction. Table 1 shows that the distribution of C. pipiens predicted by the MaxEnt model was most affected by three environmental factors: yearly precipitation (mm) (bio12, 8.7%), temperature seasonality (bio4, 31%), and altitude (Alt, 60.3%). According to the Jackknife results (Figure 2), altitude (Alt, 60.3%), temperature seasonality (bio4, 31%), and annual precipitation (mm) (bio12, 8.7%) were the main environmental parameters influencing the habitat of C. pipiens. The main factors that limited the selection of suitable stations for C. pipiens were temperature fluctuations, precipitation, altitude, and temperature.

The MaxEnt model’s internal Jackknife test was used to estimate which variables contribute most to the model development.

We reported the mean and range values for factor contributions and species–environment relationship curves. To assess the importance of individual variables for MaxEnt predictions, we conducted a Jackknife test, which provides training, test, and AUC gains for three scenarios: (1) excluding a variable, (2) including only a single variable, and (3) using all variables. The Jackknife test has been widely utilized in previous research to evaluate the significance of environmental predictors [40,42,43]. The probability of C. pipiens presence in the USA might be evaluated based on the response curves for environmental factors in MaxEnt. Low areas within an acceptable range (about 0 to 500 m) are preferred for the occurrence of C. pipiens, and this possibility steadily decreases as altitude (Alt) increases. Temperature seasonality (bio4) increased the probability of C. pipiens presence, which then progressively declined until reaching a variation of 9.3% (Figure 3). The annual precipitation (mm) (bio12) that was suitable for C. pipiens growth was raised to 1200 mm and then progressively decreased.

3.5. Climatic Suitability of C. pipiens Under Present and Future Climate Change with Impacts to Present Possiblel Distribution

The MaxEnt models, utilizing three bioclimatic factors, predicted the climatically favorable areas for the establishment of C. pipiens under both current and future climate scenarios, with varying levels of accuracy. The results indicate that the current potential distribution of C. pipiens in the United States, as shown in Figure 4, is primarily concentrated in Michigan, Indiana, Illinois, Ohio, Kentucky, Oklahoma, Missouri, Mississippi, Alabama, Virginia, North Carolina, South Carolina, Georgia, and smaller portions of Louisiana and Iowa. Additionally, the other regions that were identified as highly suitable for the potential establishment of C. pipiens include Pennsylvania, Maine, Vermont, New Hampshire, Massachusetts, Connecticut, Tennessee, and the southwest region of Wisconsin. Areas in Texas, central Kansas, and eastern and southern Minnesota were found to be moderately appropriate for C. pipiens. In contrast, North Dakota, South Dakota, New Mexico, Montana, Nevada, Utah, and northeast Alaska were deemed unfavorable locations for C. pipiens in the United States.

3.6. The Predicted Future Potential Distribution Areas for 2050 and 2070

The regions that are anticipated to be suitable for C. pipiens in 2041–2060 and 2061–2080 are shown in (Figure 5) based on climatic data under SSP1-2.6 and SSP5-8.5, as reported by BCC-CSM1.1 (Beijing climatic Centre–Climate System Modelling 1.1). The distribution patterns between the present and future climate scenarios showed a high degree of similarity, with only a few exceptions. Furthermore, there were some differences between SSP1-2.6 and SSP5-8.5 in the 2050 and 2070 projections (

Table 3. Percentage contribution of specific bioclimatic variables to the climatic suitability models.

Variable	Alt	bio4	bio12
Current Climate (%)	60.3	31	8.7
BCC-CSM1_ssp126_2041-2060 (%)	63.7	18.9	17.3
BCC-CSM1_ssp126_2061-2080 (%)	59.2	30.7	10.2
BCC-CSM1_ssp585_2041-2060 (%)	61.6	28.5	9.9
BCC-CSM1_ssp585_2061-2080 (%)	65.6	26.2	8.2

).

4. Discussion

The most significant bioclimatic factors influencing the presence of Culex pipiens were temperature, altitude (Alt), seasonality (bio4), and annual precipitation (bio12, in mm). These findings are consistent with previous research [7]. Numerous factors may have a major impact on C. pipiens distribution. The primary impacts of climate change on endemic mosquitoes are changes in rainfall patterns and increasing temperatures. Generally speaking, an increase in precipitation increases the amount of possible habitat for mosquitoes to deposit their eggs and grow their larvae. The relationship is often nonlinear; above-average rainfall usually boosts mosquito numbers by increasing the availability of standing water, whereas strong or violent precipitation can have a leaching impact and destroy the eggs and flush larvae from certain habitats [39]. The development of juvenile mosquito lifecycle phases may be accelerated by higher temperatures, leading to exponential population expansion and higher rates of reproduction [40,51]. For example, seasonal temperatures above average in Canada appear to increase the frequency of WNV infection outbreaks because they encourage prolonged host-seeking by female mosquitoes that may be infected and accelerate virus accumulation in vector mosquitoes. Because the extrinsic incubation period is shortened by these high temperatures, infected mosquitoes become infectious more quickly [52]. In Korea and Japan, the transmission season can be extended by many months with a mere 5 °C increase in normal summer temperatures [53]. Rainfall fluctuations affect the amount of standing water available for mosquitoes to lay their eggs and grow into young. As a result, mosquito reproduction and survival are significantly impacted by changes in rainfall [54]. When comparing the predictions of different models under the same conditions, variations in the forecast results were observed. For instance, in the current scenario, the BCC-CSM1 model predicted a decrease in the moderately suitable areas for C. pipiens in the future. However, the future model predictions suggested a slight increase in these areas. Climate change will undoubtedly have an impact on the future dissemination of viruses by endemic mosquitoes, as endemic species—including those associated with arboviruses—increase in quantity and population. It is challenging to forecast how MBDs will respond to these changes because mosquitoes, reservoirs, and the environment all depend on climate change in different ways. This suggests that relatively small changes in the climate may lead to large increases in arbovirus transmission. Additionally, each MBD has unique transmission cycles, vectors, and reservoirs. The prevalence of MBD will differ by region, habitat, and location because these may only be found in a few numbers of places in the US. The prevalence of WNV and other endemic arboviruses is predicted to increase in both rural and urban regions. More research is required to determine how climate change might affect WNV. Increasing surveillance capabilities will be essential when arboviruses expand to new areas. To better understand the changing and potentially expanding dynamics of arbovirus transmission, research studies should also be conducted.

Policy Implications from This Study

Our findings provide an evidence base for climate-informed public health policies which that can be used to manage the expanding distribution of C. pipiens, a primary vector of WNV and other MBDs into new areas in the US. Public health management can integrate Adaptive Resource Management (ARM) and Integrated Pest Management (IPM) into their risk management responses and regulatory frameworks. This will allow public health authorities to better address the challenges posed by climate change affecting MBDs.

ARM provides a mutable policy tool for adjusting public health strategies based on real-time data and changing environmental conditions. For example, predictive distribution models for C. pipiens can be integrated with climate projections to create early warning systems. To achieve this integration regulatory agencies, such as the U.S. Centers for Disease Control and Prevention (US CDC), could establish regions predicted to be affected by MaxEnt models with specific mosquito surveillance networks. The aim would be to monitor mosquito populations and WNV activity in newly suitable habitats predicted by modeling. These systems would enable the timely deployment of resources, such as vaccines or vector control measures, in areas experiencing changes in mosquito population dynamics and increased virus transmission risk.

Currently, there are a few mosquito surveillance system such as ArboNet a national surveillance system for arboviruses, (US CDC) [55] and VectorSurv a west coast web-based platform capable of supporting mosquito surveillance with geospatial tools (University of California, Davis and California Vectorborne Disease Surveillance System-CalSurv) [56]. Both surveillance platforms are fit for purpose with the ability to monitor WNV [55,56]. The surveillance platforms serve as an information hub to guide public health responses at local levels.

IPM can assist ARM by providing a sustainable and multi-faceted approach to mosquito control where and when needed based on surveillance and modeling outputs. For example, IPM strategies for WNV could include targeted chemical larvicide applications in high-risk areas [57,58] and the use of biological controls (e.g., mosquito-eating fish [59], essential oils-plant extract mixtures [60], Wolbachia-infected mosquito release [61], Bacillus thuringiensis –BT toxin sprays [62], fungal pathogens (eg. Lagenidium giganteum motile zoospores) [63], parasites (eg. Vavraia culicis) [64] and Sterile Insect Technique (SIT) [65]. Other IPM efforts can include community-based efforts to reduce breeding sites (eg. standing water removal and container removal programs) [66]. Funded programs that incentivize biological control measures have proven successful for mosquito control in the US [66].

Aligning ARM and IPM with real-time surveillance and timely adaptive management responses will allow public health agencies to mitigate WNV transmission hot spots while building long-term resilience against the broader impacts of climate change on MBD dynamics. These strategies, informed by spatial and climate models, such as the analysis we have carried out, provide a way forward to identify areas of future suitable habitat for ARM and IPM initiatives to reduce arbovirus MBD transmissions.

5. Study Limitations

The study is limited by the reliance of historical surveillance data and how data were collected. Our occurrence data for C. pipiens were taken from the Global Biodiversity Information Facility (GBIF), which while comprehensive, may be subject to sampling bias and uneven spatial distribution. The uneven sampling could affect the accuracy of the species distribution model, particularly in under-sampled regions. We used data cleaning procedures, including removal of duplicates and erroneous points, to improve the data set but these inherent biases in the data remain a limitation.

Second, we used a spatial resolution of 2.5 arcminutes (~5 km × 5 km at the equator) which could be considered a limitation for fine-scale ecological studies. This resolution may not adequately capture micro-habitat variations that influence mosquito breeding sites and distribution, particularly in heterogeneous landscapes (areas with small rivers, ponds, quarries, or culverts within the 25 square km area).

Third, MaxEnt assumes that sampling is uniform across the study area, which is rarely the case for historical data. This assumption can affect the model’s ability to represent true species–environment relationships.

Fourth, MaxEnt uses presence-only or mosquito occurrence data, which may result in over-prediction in regions where environmental conditions are suitable, but the species is absent due to other biotic or historical constraints.

Fifth, overfitting is a common MaxEnt issue. To mitigate the risk of overfitting associated with MaxEnt, we applied regularization and excluded highly correlated variables using VIF (variance inflation factor) analysis. Despite our correction some degree of overfitting may persist.

While there are limitations, we believe that the data set curation, analysis to select the most important climatic variables, and steps taken for model evaluation (e.g., AUC values) minimize the limitations as much as possible given the historical data set.

6. Conclusions

Climate change is anticipated to significantly impact endemic mosquito populations in the United States, thereby influencing the transmission of mosquito-borne diseases (MBDs) such as West Nile virus (WNV). The risk maps suggest that Culex pipiens may expand its range northward and potentially westward over the coming decades. These maps underscore the potential for range shifts in C. pipiens and emphasize the necessity for ongoing surveillance and monitoring to track changes in vector distribution. This will be essential for adapting to the evolving disease risks associated with projected climate change.

Our model outputs show the potential for C. pipiens to invade new geographic areas under future climate scenarios. This will directly correlate with increased exposure risks to mosquito-borne diseases like WNV. Shifts in mosquito host ranges are expected to place additional strain on public health systems. Response to MBDs will require allocated funding for new or enhanced surveillance, enhanced vector control programs, and healthcare resources to manage outbreaks. The economic burden related to the invasion of new areas includes human costs (morbidity and mortality from MBDs), medical costs for treatment, vector management efforts, and potential losses in outdoor labor productivity due to illness, all which underscores the wider range of societal implications.

In order to implement risk management control programs for these pests, which have negative impacts on both human health and the animal sector, decision-makers and public health officials may find the modeled results helpful. Our findings demonstrate the necessity of taking preventative action to lessen the long-term effects of climate change on the appropriateness of MBD habitat. The following are important ramifications and alternatives for future risk management: The first method of managing C. pipiens populations is the application of different pest management techniques, such as biological control, larvicides, and adulticides, through the use of Integrated Pest Management (IPM). To lessen misuse or reliance on discriminatory chemical control measures alone, which may have detrimental environmental effects, an integrated approach can combine selective larviciding, fogging, and spraying with other biological techniques [67].

Second, the dynamic nature of climate change and vector dispersal necessitates the use of Adaptive Resource Management (ARM) approaches. Continuous observation and monitoring of mosquito populations, disease incidence, and environmental factors provide an empirical foundation for risk reduction efforts. For improved occupational and public health outcomes, decision-makers should be prepared to use ARM, for example, to safeguard worker health and adjust control mechanisms in reaction to real-time data and new trends [68].

Third, the establishment of alert surveillance networks will provide strong monitoring in areas that are anticipated to be ideal for C. pipiens. According to [69], these networks can serve as an early warning system, enabling authorities to start IPM and ARM as soon as disease vectors arrive. Disease outbreaks can be avoided, and their effects can be reduced with effective surveillance [70]. For real-time surveillance of mosquito disease vectors, surveillance networks are investigating the application of novel techniques like artificial intelligence (AI) and the Internet of Things (IoT) [71,72].

Fourth, in order to increase public awareness of the dangers of mosquito-borne illnesses and the part that individuals play in preventing them, public education and awareness programs for protection against WNV should be launched [73]. By removing standing water in containers and tires that have been discarded, as well as by employing preventative measures against mosquito bites (such as body covering, repellents, or limiting time outside during periods of high mosquito activity), communities can be motivated to take steps to reduce mosquito breeding sites [74].

Fifth, greater coordinated efforts at the local, national, and international levels are needed for collaboration, data sharing, and information sharing. The plan focuses on enhancing cooperation among public health authorities, researchers, environmental organizations, and policymakers to enable more effective approaches to disease prevention and management [75] in addition to awareness education campaign [76,77].

Effective risk management of WNV and other arboviral MBD is hampered by the predicted range extension of C. pipiens brought on by climate change. Human reaction and adaptability to shifting MBD transmission dynamics from climate change can be enhanced by the application of different control mechanisms with well-defined action thresholds and efficient public health risk communication. This study serves as a catalyst for decision-makers to coordinate their management reaction and create more resilient and comprehensive strategies to safeguard human health.