Environmental correlates of Aedes aegypti abundance in the West Valley Region of San Bernardino County, California, U.S.A. from 2017- 2023: an ecological modeling study

Environmental correlates of Aedes aegypti abundance in the West Valley Region of San Bernardino County, California, U.S.A. from 2017- 2023: an ecological modeling study | 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 Environmental correlates of Aedes aegypti abundance in the West Valley Region of San Bernardino County, California, U.S.A. from 2017- 2023: an ecological modeling study Gaëlle Tatiana Sehi, Solomon Kibret Birhanie, Jacob Hans, Michelle Q. Brown, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6280539/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Parasites & Vectors → Version 1 posted 9 You are reading this latest preprint version Abstract Aedes mosquitoes, particularly Ae. aegypti and Ae. albopictus , are major vectors of globally significant diseases such as dengue, Zika, and chikungunya. Since 2013, Ae. aegypti populations have rapidly expanded in California, making control efforts difficult because of their cryptic breeding sites and urban habitat preference. Remote sensing technologies, coupled with Geographic Information Systems (GIS), offer innovative solutions for mosquito surveillance and control. However, understanding the environmental drivers of mosquito abundance, particularly in California’s diverse ecological settings, remains a critical gap. To address this gap, we analyzed Ae. aegypti abundance (2017 to 2023) in relation to environmental variables, such as temperature, precipitation, surface water, elevation, and built environment. We applied hotspot analysis to identify spatial clusters of high mosquito abundance and used a generalized additive model (GAM) with a negative binomial distribution to assess environmental and meteorological influences on mosquito counts. Hotspot analyses revealed clusters of Ae. aegypti hotspots near residential areas. Ae. aegypti counts increased with higher surface water availability and temperature. Our study elucidates the complex dynamics of Ae. aegypti mosquito abundance in the West Valley Region of San Bernardino County from 2017 to 2023, shedding light on the influence of environmental factors and human activities on temporal trends. Our findings emphasize the critical role of temperature and water availability in shaping mosquito population dynamics, highlighting the need for proactive vector control strategies in response to environmental changes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Aedes mosquitoes (especially Ae. aegypti and Ae. albopictus ) are among the most important vectors of human disease globally. 1 They are the major vectors for several epidemiologically important viruses, including dengue, Zika, and chikungunya viruses. In most regions, the primary public health approach to controlling Aedes -borne diseases is vector-focused. 2 Understanding the ecology and spatio-temporal abundance of mosquito vectors can be important for targeting vector-focused interventions. Environmental and meteorological factors have a major impact on the geographic distribution and abundance of mosquitoes. As ectotherms, ambient temperature has a non-linear association with mosquito activity, development, and population growth. 3 Temperatures that are too cold or too hot can negatively impact mosquito development and population growth. 4 – 6 Optimal temperatures can increase development and population growth. 7 The juvenile stages of mosquitoes are aquatic, meaning that water is crucial for mosquito habitation and population growth. Precipitation is therefore frequently associated with increased mosquito populations as it often leads to increased bodies of water that can serve as the necessary aquatic environment for the juvenile stages of mosquitoes. Other ecological and environmental factors are likewise important. Ae. aegypti is a container-breeding mosquito, preferring to lay eggs and spend its aquatic stages in human-made or naturally occurring containers that hold small bodies of water (e.g. cups, cans, old tires, bromeliads, tree holes). 8 Given an optimal temperature and the presence of preferential bodies of water, Ae. aegypti can thrive in urban and suburban settings. While this mosquito species is known to have relatively short flight ranges (normally shorter than 200m) it can disperse through human transportation routes, for example through cargo ships. 9 Once this species has invaded a geographic region with suitable temperature and aquatic habitats, it is difficult to control for a variety of reasons (e.g. many small cryptic habitats across a landscape can be difficult to find). Ae. aegypti mosquitoes have been detected in California since 2013, and their range and abundance appear to be expanding. 10 – 12 By the end of 2023, the species had been detected in over 300 cities within 24 central and southern counties of the State. 11 Because of its vectorial capacity, urban-dwelling nature, and its anthropophilic behavior, 13 it poses a significant risk of Aedes -borne disease transmission. The presence of this mosquito vector species locally breeding has alarmed vector control agencies about the possibility of establishment of local transmission of Aedes -borne disease. Indeed, such local transmission has already occurred in Florida, 14 , 15 , and in 2024, 16 locally acquired dengue cases have been reported in southern California. 16 With travel-related Aedes -borne diseases on the rise, 17 the risk of importation and local transmission is high and growing. This study aims to examine granular spatial and temporal patterns of Ae. aegypti from 2017 to 2023 and to identify important environmental and meteorological factors associated with its abundance in the West Valley Mosquito and Vector Control District of San Bernardino County (Fig. 1 ). Since this species is of public health importance, an understanding of the factors that influence its geographic and temporal distribution is important for targeted mosquito control efforts. METHODS Study Area The study was conducted in the West Valley Mosquito and Vector Control District (WVMVCD) of southwestern San Bernardino County, encompassing six cities: Chino, Chino Hills, Ontario, Upland, Montclair and Rancho Cucamonga, with a total estimated population of 600,000 (Fig. 1 ). The area is characterized by a Mediterranean climate, experiencing hot, dry summers and mild, wet winters. 18 Seasonal precipitation occurs predominantly between November and April, with occasional summer thunderstorms. Summer temperatures in the West Valley MVCD frequently surpass 38°C, with occasional spikes exceeding 42°C (Figure S1 & S2). The native vegetation includes chaparral, oak woodlands, and riparian habitats, although urbanization has led to significant alterations in the landscape. 19 Furthermore, the region has experienced rapid population growth in recent decades, accompanied by extensive urban development, including residential, commercial, and industrial structures. Adult mosquito trapping Weekly mosquito surveillance was carried out by the West Valley Mosquito and Vector Control District (WVMVCD), a special district responsible for vector surveillance and control in the region. Mosquito collections took place from January 2017 to December 2023 using BG-Sentinel traps (Biogents AG, Regensburg, Germany), which were deployed weekly, totaling 6,794 trap nights (Fig. 2 ). BG-Sentinel traps are recognized for their effectiveness in monitoring Ae. aegypti across spatial and temporal trends. 20 To enhance mosquito attraction, the traps were equipped with BG-Lure (Biogents AG), an artificial human scent, and a bucket containing 1 kg of dry ice. Trapping was systematically conducted throughout the district and set in residential areas, with the homeowner's consent traps were left overnight and mosquitoes were collected the next morning, and then counted and sorted by species. Each trap included a geographic location (latitude and longitude) and a timestamp indicating when it was set. Trap sites were strategically chosen based on historical mosquito hotspots, service requests from residents (e.g., complaints about mosquito biting), and an exploratory strategy to monitor mosquito activity across different areas of the district. All traps were brought to the WVMVCD Lab for mosquito counting and species identification by trained technicians. The District maintains records of adult mosquito counts, categorized by location and date, from 2017 to 2023 Environmental and Meteorological Variables Environmental and meteorological data were obtained through the Google Earth Engine (Table 1 ) from a variety of Earth observation data sources. 21 We extracted data on built environment, elevation, precipitation, normalized difference water index (NDWI, a measure of surface water or moisture), and average ambient temperature - all chosen for their hypothesized or known impact on mosquito abundance (listed in detail in Table 1 ). These data were downloaded and merged into the trapping dataset based on location and trap date. Buffers of 150m in size were drawn around each of the trap locations and environmental and meteorological variables were attributed to the traps based on these buffers. This buffer size was chosen based on the assumption that Ae. aegypti dispersal is generally limited to within 100 meters of their breeding sites. 22 In our exploratory analyses we also tested the use of 250m buffers, based on a study from central California that suggested this species may have broader dispersal in nearby areas. 23 We extracted precipitation, surface water, and average temperature with 14-day and 28-day lag periods from the mosquito collection date to account for the time needed for these environmental factors to influence mosquito development and population dynamics. 24 Table 1 Description of variables included in the final model Variable Description Source Date Range Frequency Outcome Total Aedes Counts of adult Ae. aegypti per trap WVMVCD Jan, 1, 2017 - Dec, 31, 2023 Weekly Meteorological Precipitation (mm) †* Log- transformation of mean precipitation per trap Copernicus Climate Change Service (C3S) ERA5-Land provides precipitation data with a resolution of 9 km. 25 Dec, 1, 2016 - Dec, 31, 2023 Daily Average. temperature (K) †* Mean maximum temperature in per trap in Celsius (based on buffer around trap) Copernicus Climate Change Service (C3S) ERA5-Land provides precipitation data with a resolution of 9 km. 25 Dec, 1, 2016 - Dec, 31, 2023 Daily Temporal Day of the year Day of the year on which the trap was checked (Day 1 was the first day of the study year and day 365 was the last) NA Jan, 1, 2017 - Dec, 31, 2023 Daily Year Year of the study NA 2017–2023 Environmental factors Surface water †* Mean surface water index per trap (based on buffer around trap) Sentinel-2 MultiSpectral Instrument provides NDWI index value with a resolution of 30 meters. 26 Dec, 1, 2016 - Dec, 31, 2023 Every 5 days Elevation † Mean elevation from buffer around trap NASA Shuttle Radar Topography Mission Global 1 arc second (SRTMGL1) product, with a resolution of approximately 30 meters. 27 2023 Constant Built area † Total number of pixels classified as ‘built’ within the 150m radius of each respective trap per year Dynamic World provides near real-time (NRT) land use land cover (LULC) with a resolution of 10 meters. 28 2017–2023 Yearly (taken from September) Longitude Longitude of each trap WVMVCD Jan, 1, 2015 - Dec, 31, 2023 Constant Latitude Longitude of each trap WVMVCD Jan, 1, 2015 - Dec, 31, 2023 Constant † Mean centered and standardized. *14 and 28 days lags NA = Not applicable Statistical Analysis Temporal patterns in trapped Aedes We conducted descriptive statistics to examine counts of trapped Ae. aegypti mosquitoes over time. We investigated counts of Ae. aegypti mosquitoes per trap over years (investigating potential changes in abundance over years), as well as by month across all years (investigating potential seasonal patterns (Table 2 )). Spatial patterns in trapped Aedes We mapped counts of Ae. aegypti mosquitoes per trap during different time periods (2017–2020 and 2021–2023) to investigate general shifts in trap yield across the study period. We then employed two main approaches to examine the potential spatial patterns of Ae. aegypti abundance from 2017 to 2023. First, we created spatial correlograms for each year to investigate how spatial autocorrelation in mosquito abundance changes with increasing distance between trap locations. These correlograms were constructed by calculating Moran's I at increasing distance intervals up to 3 km, using 500-meter bins. To account for sampling effort variations, we used the mean weekly number of mosquitoes per trap location within each year for these calculations. This approach provided insights into the scale of spatial dependence and its annual variations. Following this global assessment, we employed the Getis-Ord Gi statistic to identify significant local clusters of high (hot spots) and low (cold spots) Ae. aegypti abundance. We used a distance-decay approach for the neighbor weights matrix, with a 500-meter threshold. Locations with statistically significant high positive Z-scores (> 1.96, p < 0.05) were identified as hot spots, while those with significantly low negative Z-scores (< -1.96, p < 0.05) were classified as cold spots. To ensure robustness, we conducted a sensitivity analysis by repeating the correlogram analysis and Getis-Ord Gi* using 250-meter bins/ threshold, comparing results with our primary analysis. We generated annual hotspot maps and created a combined visualization of annual spatial correlograms. This multi-approach method allowed us to track changes in spatial clustering patterns over time. Our exploratory spatial analyses provided a foundation for investigations into environmental correlates of Ae. aegypti abundance. Bivariate Analysis between environmental and meteorological covariates and Ae. aegypti abundance We hypothesized that the spatial and temporal distribution of Ae. aegypti in the WVMVCD was positively associated with precipitation, surface water, and built environment. We also hypothesized that higher ambient temperatures would be associated with higher mosquito abundance. To visualize potential associations between Ae. aegypti abundance and environmental factors, mosquito abundance data from traps were categorized into quartiles (Q1-Q4), where Q1 represents the lowest 25% of abundance counts and Q4 represents the highest 25%. We then plotted continuous environmental variables by mosquito abundance quartiles. This approach allowed for a preliminary examination of potential associations between each factor and Ae. aegypti abundance in our study area. Generalized Additive Model for associations between environmental and meteorological covariates and Ae. aegypti abundance We then used a negative binomial generalized additive model (GAM) to model counts of trapped Ae. aegypti mosquitoes, using the aforementioned environmental variables as covariates in the model and accounting for trap location and time, for the duration of the 2017–2023 study period. The GAM allowed us to account for the complex and potentially non-linear associations between environmental variables and Ae. aegypti abundance through the use of smoothing splines functions. 29 To account for potential lagged effects (i.e. precipitation might have a delayed effect on mosquito abundance), we explored different lags (14-days and 28-days) for the covariates. We began by developing a model for all data from 2017 through 2023, with all covariates specified as thin plate regression spline functions. All covariates were centered on their means and standardized by their standard deviation to facilitate the interpretation and comparison of effect sizes. Final variable selection and model specification was based on a combination of scientific hypotheses, model goodness-of-fit tests (deviance explained and the Akaike Information Criterion (AIC)), and interpretability. Through comparison of model fit using Deviance Explained and Akaike Information Criterion (AIC), we determined that a 14-day lag for these variables provided the best model fit. The final negative binomial GAM included the following variables: NDWI, precipitation, built environment, elevation, average temperature, geographic location, season, and year. It also incorporated interaction terms between season and average temperature, as the effect of temperature shifted over time. The results of our regression analysis are presented as graphs showing the estimated smoothed spline functions for each predictor variable, along with a summary table indicating statistical significance. Software All statistical analyses were performed using the R statistical software (version 4.3.3). The spatial autocorrelation analyses were run using spdep and tmap packages. The GAM was created using the mgcv package. All maps were created using QGIS (version 3.34.9). RESULTS Temporal distribution of trapped Ae. aegypti mosquitoes The abundance of Ae. aegypti has increased in this setting over the study period (2017–2023), even while accounting for increased surveillance and trapping. While only 358 adult Ae. aegypti were trapped in 2017; 2,954 were trapped in 2019 and 30,147 were trapped in 2023. While trapping and surveillance increased from 682 traps set in 2017 to 1,538 in 2023, the mean and median numbers of Ae. aegypti caught per trap night increased from 0.52 and 0 (mean and median respectively) per trap in 2017 to 19.6 and 7 per trap (mean and median respectively) in 2023. The proportion of traps set that did not catch any mosquitoes also decreased over this period, from 91% in 2017 to 23% in 2023 (Table 2 ). There was a clear seasonal pattern in trapped adult Ae. aegypti mosquitoes as well (Table S3). August, September, and October had the highest numbers of trapped mosquitoes (higher mean per trap, higher median per trap, and lower proportion of traps catching zero adult Ae. aegypti ). This time period roughly corresponds to the local dry and hot season (Supplemental Figures S1 and S2). Table 2 Summary statistics for trapped adult Ae. aegypti and traps by year Year Total Ae. aegypti caught Total number of traps Mean per trap sd per trap Median per trap IQR per trap Proportion of traps with no mosquitoes 2017 358 682 0.52 3.07 0 0 0.91 2018 661 750 0.88 2.59 0 0 0.76 2019 2954 772 3.83 7.28 1 5 0.44 2020 6404 898 7.13 11.24 2 9.75 0.30 2021 7953 991 8.03 15.77 3 11 0.31 2022 17133 1009 16.98 41.97 6 19 0.25 2023 30147 1538 19.60 32.43 7 24 0.23 Spatial patterns in trapped Ae. aegypti mosquitoes The increase in abundance of trapped Ae. aegypti in the WVMVCD during the study period (2017–2023) was likewise evidenced in maps of trapped mosquitoes (Fig. 2 ). Trapping and surveillance have expanded at the same time, but this does not account for the increase in abundance. Spatial correlograms for Ae. aegypti from 2017 through 2023 indicate positive spatial autocorrelation (measured using Moran's I statistic) at relatively small distances (especially < 500 meters) (Fig. 3 ). All but one year (2018) showed positive clustering up to approximately 1km, and four years had clustering at 500m or less. No clear and consistent pattern of clustering was apparent at distances larger than 1km. Additionally, our spatial analysis using the Getis-Ord Gi* statistics with a 500-meter bin revealed dynamic patterns in Ae. aegypti abundance from 2017 to 2023 (Fig. 4 ). In the early years, hot spots were concentrated in the south (i.e. near Chino Hills State Park and residential areas). Over time, these clusters shifted and new hot spots emerged in western, central, and northern parts of the study area, encompassing diverse land use types including parks, residential, and business zones. Notably, the Chino Hills State Park hotspot persisted for two years and then transitioned from a hot spot to a cold spot by 2019. While some hot spots showed temporal stability from 2020 to 2022, several became cold spots by 2023. Correlations between meteorological and environmental variables and Ae. aegypti abundance The bivariate analysis indicated positive associations between temperature, seasonality, and recent years with higher mosquito abundance quartiles, while elevation showed a negative association (Fig. 5 ). Precipitation displayed a complex pattern, and surface water and built area showed no clear relationships across the abundance quartiles (Q1–Q4). Temperature (Fig. 5 A) showed a positive association with mosquito abundance, with higher abundance quartiles (Q3 and Q4) corresponding to higher average temperatures. This pattern was similar to the observed seasonality (Fig. 5 E), where higher abundance quartiles clustered around mid-year. Temporal trends (Fig. 5 D) were observed, with earlier years (2020–2022) correlating with higher abundance quartiles. Precipitation (Fig. 5 B) exhibited a complex relationship with abundance. All quartiles displayed consistently low median values, while higher abundance quartiles showed greater skewness and outliers. NDWI (Fig. 5 C) and built area (Fig. 5 F) displayed minimal variation across the abundance quartiles, showing no clear association with mosquito abundance in this context. Elevation (Fig. 5 G) demonstrated a slight negative association with abundance, with higher abundance quartiles occurring at slightly lower elevations. Results from the negative binomial GAM for environmental and meteorological correlates of Ae. aegypti abundance The spline interaction term for latitude and longitude (Fig. 6 A) is a representation of the association between location and abundance of Ae. aegypti , after accounting for the other covariates in the model. This map demonstrates the clustering of mosquito abundance in the south and central-east parts of the study area, as indicated by the bright white and yellow areas on the map. Surface water (Fig. 6 B) and average temperature (Fig. 6 F) showed positive associations with Ae. aegypti abundance. There was a positive association with elevation at low to mean levels of the variable, but all higher elevation areas showed no statistical significance (Fig. 6 C - confidence bands on both sides of the 0 point on the y-axis beginning at roughly 0.5 standard deviations above mean elevation and extending to all higher elevations). The model showed no association between the built environment ("Urban area" Fig. 6 D) and mosquito abundance, or with precipitation and Ae. aegypti abundance (Fig. 6 E). A strong seasonal pattern was evident (Fig. 6 G), with higher abundance occurring during August, September, and October of each year. Furthermore, the general increase in abundance over time (years) was apparent in the model (Fig. 6 H) indicating an increase in Ae. aegypti abundance over the study period (2017–2023), even after accounting for the other covariates in the model. Finally, the interaction between average temperature and day of the year (Fig. 4 I) revealed that temperature affects Ae. aegypti abundance varied throughout the year. In early spring (March-April), lower temperatures corresponded with lower mosquito counts. During the summer months (May-September), higher temperatures were associated with increased abundance. This relationship extended into early autumn, with elevated counts persisting for a short period after temperatures began to drop in October. Temperature ranges varied significantly across seasons, with lower temperatures during peak summer months (July and August) exceeding the highest temperatures observed during the coldest months (December and January). We ran a sensitivity analysis using 28-day lags for the time-varying covariates (see Table S3 and Figure S3). The model was largely the same with the exception that the ambient temperature from 28 days before a trap date was not a significant predictor of mosquito abundance (whereas in the main model presented here, ambient temperature 14 days before trapping was a significant predictor). Discussion Our study on Ae. aegypti mosquito abundance from 2017 to 2023 in the WVMVCD (part of San Bernardino County) in southern California revealed several key findings. We observed a significant increase in Ae. aegypti abundance over the study period, from 0.52 per trap in 2017 to 19.60 in 2023, with yearly increases corresponding to summer months. We found that surface water, temperature, seasonality, and geographic location were predictors associated with mosquito abundance. Additionally, spatial clustering of high mosquito abundance was concentrated in the central and southern regions of the study area - though clusters did shift over time. In our analysis, surface water was associated with Ae. aegypti abundance. Conversely, precipitation was not associated with abundance. The region is semi-arid with very little precipitation in an average year. The lack of a clear relationship between precipitation and Ae. aegypti abundance could be explained by the ecology of this species. This mosquito often breeds in small artificial containers located around human dwellings, meaning seasonal patterns in population dynamics might be more influenced by variations in peridomestic water availability than changes in precipitation. Heavy rains could likewise have a negative effect on larval abundance, since intense rainfall may flush the larval habitat, thereby affecting the adult mosquito population. 30 , 31 We also note that in this setting, surface water does not come from precipitation alone since patterns in surface water over time did not align with precipitation. We hypothesize that much of this surface water is from residential yard watering and agricultural irrigation, which increases during prolonged periods of the dry season without precipitation. While surface water availability may serve as a potential predictor of Ae. aegypti distribution, its influence is dependent on additional environmental and anthropogenic factors. We found that average temperature was also an important factor with regard to the abundance of Ae. aegypti mosquitoes. In our model, average temperature significantly influences Ae. aegypti abundance, but it is important to note that seasonal factors, including photoperiod, could modulate the effect of temperature on mosquitoes. The interaction between temperature and season highlights the complexity of mosquito population dynamics, where optimal conditions depend on multiple environmental factors. 32 While warmer temperatures during peak season generally promote mosquito development, extreme heat can reduce habitat suitability. Longer daylight hours in summer enhance mosquito activity and reproduction, whereas shorter daylight hours in cooler months suppress it, even when temperatures remain favorable. 33 These findings emphasize the need to consider both temperature and seasonal cues in predicting mosquito populations to improve vector control strategies. Geographic location plays a crucial role in Ae. aegypti abundance (Fig. 6 A). We did not find a significant association between built area and mosquito abundance in our model, likely because most of the traps were set at land use categorized as urbanized. However, using spatial analysis we identified localized spatial clustering of Ae. aegypti abundance in various locations across the study area. For example, the spatial correlogram analysis showed that Ae. aegypti populations are highly localized and the Getis-Ord analysis revealed spatial-temporal shifts in Ae. aegypti abundance, with initial hotspots concentrated in the southern part of the study area in 2017 gradually expanding and relocating to central and northern regions in subsequent years. Our regression analyses suggest that some of these spatial and temporal patterns are driven by micro-environmental factors such as water availability, ambient temperature, and human activity. 34 Many of these hot spots were observed in residential areas and near public spaces such as parks and schools, likely due to the presence of suitable breeding sites and human hosts. Clusters of high mosquito abundance were also noted in proximity to commercial areas, including real estate developments, fire sprinkler system businesses, junk removal services, and major roads (e.g., Highways 210 and 110). Cold spots, while less frequent, were observed in some locations, including near a senior center. The distribution of both hot spots and cold spots varied throughout the study period, with some areas showing persistent patterns and others demonstrating changes over time. The spatial and temporal variability in Ae. aegypti abundance has important implications for vector control strategies. Persistent hotspots may require targeted, intensive interventions, while areas with fluctuating patterns suggest the need for adaptive, flexible approaches. Understanding these dynamics is critical for optimizing resource allocation in mosquito control efforts, whether through traditional methods, novel techniques like sterile insect release, or integrated vector management strategies. This knowledge can guide decision-makers in prioritizing high-risk areas, adjusting intervention timing and intensity, and potentially improving the cost-effectiveness of control measures. Ultimately, these insights contribute to more efficient and sustainable vector management, which is critical for reducing the risk of mosquito-borne diseases in urban environments. The spatial and temporal patterns we observed in Ae. aegypti abundance reflects the complex dynamics of this species' expansion into new environments. Ae. aegypti has been expanding in southwestern parts of the United States in recent years, 35 a trend of particular concern given the large population in California, the relatively frequent travel-related Aedes- borne diseases, and the recent occurrence of locally transmitted dengue in the region. 36 The relationships that we observed, which partially differ from what is normally seen in other tropical and subtropical regions of the world where the mosquito is endemic (and normally more common in the rainy season), highlight potential challenges for public health and vector control agencies. Heightened surveillance that includes ecological research into the environmental, meteorological, and geographic correlates of Ae. aegypti abundance is important for understanding seasonal, interannual, and geographic patterns in abundance. As this species moves into new environments, identifying consistent environmental predictors of its presence and abundance may remain challenging until it reaches equilibrium with local environmental conditions. Limitations and Strengths This study has both limitations and strengths. Our assessment of mosquito abundance is limited to locations and time periods when trapping occurred (February to November). Over time, trapping efforts expanded geographically, coinciding with an increase in Ae. aegypti abundance. While our model accounted for trapping intensity, we were unable to assess abundance in areas and time periods without trapping. Additionally, trapping itself may influence Ae. aegypti abundance, as mosquitoes removed from the environment are not replaced immediately. 20 Nonetheless, we believe the trapped mosquitoes provide a reliable representation of abundance in the surrounding environment. Our analyses incorporated Earth observation data, primarily derived from satellite imagery. These data can be affected by cloud cover, potentially leading to incomplete surface water measurements. 37 However, unlike tropical regions where cloud cover can persist for weeks or months, this issue is relatively minor in this arid part of North America. There were relatively few periods during the study when Earth observation data were unavailable. Despite these limitations, this study has several notable strengths. Earth observation sensors (e.g., satellite systems) enabled remote monitoring of environmental attributes at fine spatial and temporal resolutions throughout most of the study period. 38 Comparable on-the-ground measurements would have been cost-prohibitive and labor-intensive, making long-term monitoring impractical. As more Earth observation data become available, their integration into vector-borne disease surveillance in this and other settings will likely expand. 39 Moreover, the extensive surveillance data collected over eight years represent a major strength of this study. The richness of these spatial and temporal data enabled a robust analysis of mosquito distribution patterns, revealing both long-term trends and localized variations. Finally, as the threat of Aedes -borne diseases in this region increases vector control agencies have moved toward the implementation of different novel interventions. 36 In the West Valley MVCD, sterile insect release is currently being incorporated into vector control efforts. 40 , 41 A recent work indicated a reduction in Ae. aegypti abundance by up to 65% following the optimization of integrated vector management strategies through adoption of target sterile insect technique and In2Care Mosquito Stations in selected sites in the area in 2024. 42 The model presented here, which accounts for environmental factors influencing Ae. aegypti abundance, can be adapted to assess the relative impact of this intervention while accounting for spatial and temporal variability. Conclusion In conclusion, this study highlights a significant increase in Ae. aegypti abundance in West Valley MVCD of San Bernardino County over the past eight years, and highlights complex relationships between environmental, meteorological, and human-driven factors and their abundance. Our findings emphasize the importance of surface water—potentially driven by human activities such as garden irrigation—alongside temperature and seasonality as key predictors of mosquito abundance in this arid region. The integration of long-term surveillance data with satellite-derived environmental metrics provides a strong foundation for ongoing research and monitoring efforts, which will be important as the region is now incorporating innovative vector control approaches such as sterile insect release. Accurately modeling the relative impact of such interventions while accounting for geographic and environmental drivers of abundance will be essential. Finally, as locally transmitted Aedes -borne diseases continue to emerge in North America—including recent cases in Southern California—understanding spatial and temporal shifts in mosquito abundance will be essential for public engagement, vector control, and disease prevention efforts. Declarations Data Availability Data used in this manuscript can be requested by contacting the corresponding authors. Acknowledgments We would like to acknowledge the West Valley Mosquito and Vector Control District for the provision of the historical mosquito surveillance data. The authors acknowledge funding support from the Pacific Southwest Regional Center of Excellence for Vector-Borne Diseases funded by the U.S. Centers for Disease Control and Prevention (Cooperative Agreement 1U01CK000649). Funding This research was funded by the Pacific Southwest Regional Center of Excellence for Vector-Borne Diseases and the U.S. Centers for Disease Control and Prevention. (Cooperative Agreement 1U01CK000649). The funders played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. Ethics declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare no competing interests. References Leta S, Beyene TJ, De Clercq EM, Amenu K, Kraemer MUG, Revie CW. Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. Int J Infect Dis. 2018;67:25–35. doi: 10.1016/j.ijid.2017.11.026 Roiz D, Wilson AL, Scott TW, et al. Integrated Aedes management for the control of Aedes-borne diseases. PLOS Neglected Tropical Diseases. 2018;12(12):e0006845. doi: 10.1371/journal.pntd.0006845 Kingsolver JG, Higgins JK, Augustine KE. Fluctuating temperatures and ectotherm growth: distinguishing non-linear and time-dependent effects. 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CDPH; 2024. chrome -extension://efaidnbmnnnibpcajpcglclefindmkaj/https://westnile.ca.gov/pdfs/WeeklyUpdateDengueInfectionsCA.pdf Wong JM, Rivera A, Volkman HR. Travel-Associated Dengue Cases — United States, 2010–2021. MMWR Morb Mortal Wkly Rep. 2023;72. doi: 10.15585/mmwr.mm7230a3 State of California The Natural Resources Agency Department of Fish and Wildlife. Atlas of the Biodiversity of California. In: ; 2021:12–15. Accessed February 26, 2025. https://wildlife.ca.gov/Data/Atlas#591553771-additional-information-on-the-first-edition Syphard AD, Brennan TJ, Keeley JE. Extent and drivers of vegetation type conversion in Southern California chaparral. Ecosphere. 2019;10(7):e02796. doi: 10.1002/ecs2.2796 Staunton KM, Crawford JE, Cornel D, et al. Environmental influences on Aedes aegypti catches in Biogents Sentinel traps during a Californian “rear and release” program: Implications for designing surveillance programs. PLOS Neglected Tropical Diseases. 2020;14(6):e0008367. doi: 10.1371/journal.pntd.0008367 Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 2017;202:18–27. doi: 10.1016/j.rse.2017.06.031 Moore TC, Brown HE. Estimating Aedes aegypti (Diptera: Culicidae) Flight Distance: Meta-Data Analysis. J Med Entomol. 2022;59(4):1164–1170. doi: 10.1093/jme/tjac070 Marcantonio M, Reyes T, Barker CM. Quantifying Aedes aegypti dispersal in space and time: a modeling approach. Ecosphere. 2019;10(12):e02977. doi: 10.1002/ecs2.2977 Martin JL, Lippi CA, Stewart-Ibarra AM, et al. Household and climate factors influence Aedes aegypti presence in the arid city of Huaquillas, Ecuador. PLoS Negl Trop Dis. 2021;15(11):e0009931. doi: 10.1371/journal.pntd.0009931 Muñoz-Sabater J, Dutra E, Agustí-Panareda A, et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data. 2021;13(9):4349–4383. doi: 10.5194/essd-13-4349-2021 Pour AB, Parsa M, Eldosouky AM, eds. Geospatial Analysis Applied to Mineral Exploration: Remote Sensing, GIS, Geochemical, and Geophysical Applications to Mineral Resources. Elsevier; 2023. doi: 10.1016/B978-0-323-95608-6.00008-1 Farr TG, Rosen PA, Caro E, et al. The Shuttle Radar Topography Mission. Reviews of Geophysics. 2007;45(2). doi: 10.1029/2005RG000183 Brown CF, Brumby SP, Guzder-Williams B, et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data. 2022;9(1):251. doi: 10.1038/s41597-022-01307-4 Wood SN. Generalized Additive Models | An Introduction with R, Second Edition |. 2nd Edition. Chapman and Hall/CRC; 2017. Accessed February 2, 2025. https://doi.org/10.1201/9781315370279 Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis. 2018;12(12):e0006935. doi: 10.1371/journal.pntd.0006935 Seidahmed OME, Eltahir EAB. A Sequence of Flushing and Drying of Breeding Habitats of Aedes aegypti (L.) Prior to the Low Dengue Season in Singapore. PLoS Negl Trop Dis. 2016;10(7):e0004842. doi: 10.1371/journal.pntd.0004842 Rueda LM, Patel KJ, Axtell RC, Stinner RE. Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). J Med Entomol. 1990;27(5):892–898. doi: 10.1093/jmedent/27.5.892 Costanzo KS, Dahan RA, Radwan D. Effects of photoperiod on population performance and sexually dimorphic responses in two major arbovirus mosquito vectors, Aedes aegypti and Aedes albopictus (Diptera: Culicidae). Int J Trop Insect Sci. 2016;36(04):177–187. doi: 10.1017/S1742758416000163 Liu-Helmersson J, Rocklöv J, Sewe M, Brännström Å. Climate change may enable Aedes aegypti infestation in major European cities by 2100. Environmental Research. 2019;172:693–699. doi: 10.1016/j.envres.2019.02.026 Hahn MB, Eisen L, McAllister J, Savage HM, Mutebi JP, Eisen RJ. Updated Reported Distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the United States, 1995–2016. Journal of Medical Entomology. 2017;54(5):1420–1424. doi: 10.1093/jme/tjx088 Romo H, Danforth M, Metzger M. Chapter 4: Mosquito-borne Diseases. In: Vector-Borne Disease Section Annual Report. 2023;California Department of Public Health, Sacramento, California, 2024:15–22. Hilker T, Lyapustin AI, Tucker CJ, Sellers PJ, Hall FG, Wang Y. Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking matter. Remote Sensing of Environment. 2012;127:370–384. doi: 10.1016/j.rse.2012.08.035 Cleckner HL, Allen TR, Bellows AS. Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA. Remote Sensing. 2011;3(12):2663–2681. doi: 10.3390/rs3122663 Wimberly MC, de Beurs KM, Loboda TV, Pan WK. Satellite Observations and Malaria: New Opportunities for Research and Applications. Trends in Parasitology. 2021;37(6):525–537. doi: 10.1016/j.pt.2021.03.003 Castellon JT, Birhanie SK, Macias A, Casas R, Hans J, Brown MQ. Optimizing and synchronizing Aedes aegypti colony for Sterile Insect Technique application: Egg hatching, larval development, and adult emergence rate. Acta Tropica. 2024;259:107364. doi: 10.1016/j.actatropica.2024.107364 Birhanie SK, Castellon JT, Macias A, Casas R, Brown MQ. Preparation for targeted sterile insect technique to control invasive Aedes aegypti (Diptera: Culicidae) in southern California: dose-dependent response, survivorship, and competitiveness. J Med Entomol. Published online August 20, 2024:tjae106. doi: 10.1093/jme/tjae106 Birhanie SK, Hans J, Castellon JT, et al. Reduction in Aedes aegypti Population After a Year-Long Application of Targeted Sterile Insect Releases in the West Valley Region of Southern California. Insects. 2025;16(1):81. doi: 10.3390/insects16010081 Additional Declarations No competing interests reported. Supplementary Files SanBernSupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Parasites & Vectors → Version 1 posted Editorial decision: Revision requested 27 Apr, 2025 Reviews received at journal 26 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6280539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436376420,"identity":"aaa2e031-cbdf-4714-8cd2-789ba70c4950","order_by":0,"name":"Gaëlle Tatiana Sehi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYHACNoaEChDJwCBBgpYzJGthbIOwiNMi73/42IOH8w7L8zEwH7zNQ4wWwxtp6QaJ2w4btjGwJVsTp2UGj5kEUEsCGwOPmTRxWvrPf5NInAPSwv+NOC3yDDlsEokNYFvYiNNiIJFmJpFwLN2wjZnN2HIOUbb0H34m+aPGWl6+vfnhjTdE2XIAxmImRjnYlgZiVY6CUTAKRsHIBQAXKinAVkQBkQAAAABJRU5ErkJggg==","orcid":"","institution":"Population Health and Disease Prevention, Public Health; University of California, Irivne","correspondingAuthor":true,"prefix":"","firstName":"Gaëlle","middleName":"Tatiana","lastName":"Sehi","suffix":""},{"id":436376421,"identity":"e5d2e788-7997-4252-a45c-5935c3ca7c47","order_by":1,"name":"Solomon Kibret Birhanie","email":"","orcid":"","institution":"West Valley Mosquito and Vector Control District","correspondingAuthor":false,"prefix":"","firstName":"Solomon","middleName":"Kibret","lastName":"Birhanie","suffix":""},{"id":436376428,"identity":"fe703efe-02d8-4fc7-ac29-f24723b8c5bf","order_by":2,"name":"Jacob Hans","email":"","orcid":"","institution":"West Valley Mosquito and Vector Control District","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Hans","suffix":""},{"id":436376429,"identity":"356dacdb-4c97-4ecc-9945-27b18e832bf7","order_by":3,"name":"Michelle Q. Brown","email":"","orcid":"","institution":"West Valley Mosquito and Vector Control District","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"Q.","lastName":"Brown","suffix":""},{"id":436376430,"identity":"18bee2c3-c207-4988-8280-38d51eabddc2","order_by":4,"name":"Daniel M. Parker","email":"","orcid":"","institution":"Population Health and Disease Prevention, Public Health; University of California, Irivne","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"M.","lastName":"Parker","suffix":""}],"badges":[],"createdAt":"2025-03-21 23:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6280539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6280539/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13071-025-06967-w","type":"published","date":"2025-08-18T16:29:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80617404,"identity":"4fac927b-6183-4b0f-90b3-4bb7ab4e4b94","added_by":"auto","created_at":"2025-04-15 09:02:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Site Location: West Valley Mosquito and Vector Control District, California, USA. (Green lines)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/d4f69b2e21b0211d0ff37a5d.png"},{"id":80616906,"identity":"167a4d54-93ee-4d02-99b0-d9e3e6acb322","added_by":"auto","created_at":"2025-04-15 08:54:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":476909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of traps and adult \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAe. aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e counts: 2017-2023. \u003c/strong\u003eLocations of BG-Sentinel traps, and relative numbers of adult \u003cem\u003eAe. aegypti\u003c/em\u003e caught in traps, in three-year intervals from 2017-2023.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/334223c729b92288325db660.jpeg"},{"id":80616903,"identity":"64a074fa-a745-467f-842c-33176021c006","added_by":"auto","created_at":"2025-04-15 08:54:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial correlogram for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAedes\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Mosquitoes (2017 - 2023).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial correlogram shows Moran’s I value at varying distance classes to assess spatial autocorrelation in \u003cem\u003eAedes \u003c/em\u003emosquito abundance from 2017 to 2023. Positive Moran’s I values at shorter distances indicate the clustering of mosquito populations, with spatial dependence weakening at larger distances.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/b6bb0ecf49b97dfc0c019983.png"},{"id":80616901,"identity":"0f9fd16e-949b-4265-a624-ed9fd1977fcd","added_by":"auto","created_at":"2025-04-15 08:53:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":337006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHotspot Analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAe. aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Abundance (2017–2023).\u003cbr\u003e\n \u003c/strong\u003eThe hotspot analysis shows year-to-year differences in the spatial distribution of \u003cem\u003eAe. aegypti\u003c/em\u003e abundance across the study area. Hot spots (red) represent areas with statistically significant high mosquito abundance, while cold spots (blue) indicate areas with statistically significant low mosquito abundance, both relative to the overall study area.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/20313ccec21c53f1d23c55f7.png"},{"id":80616913,"identity":"5a624d21-045a-456a-bab0-953c53111e9c","added_by":"auto","created_at":"2025-04-15 08:54:00","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":496211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBivariate Analysis of Environmental, Meteorological, and Temporal Variables Associated with Mosquito Abundance Quartiles from 2017 - 2023.\u003cbr\u003e\n \u003c/strong\u003eBox plots displaying the median, quartiles, and outliers of mosquito abundance in relation to continuous environmental, meteorological, and temporal variables across mosquito abundance quartiles (Q1–Q4).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/46b02a8dc1ae6e7a0811c216.jpeg"},{"id":80616904,"identity":"48992d16-9171-438a-ac1e-20a02dbdb801","added_by":"auto","created_at":"2025-04-15 08:54:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpline function results from the GAM (generalized additive model) for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAe. aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e abundance from 2017 - 2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Geographic Coordinates; (B) NDWI (Normalized Difference Water Index); (C) Elevation (DEM); (D) Built Environment; (E) Precipitation; (F) Average Temperature; (G) Day of the Year (DOY, representing Seasonality); (H) Year; (I) Interaction between Day of Year and Average Temperature. As our variables are centered on their means, the x-axis in these plots represents deviations from the average value of each predictor. The y-axis shows the relative effect on mosquito abundance, centered around zero. This allows for the interpretation of how deviations from the mean of each predictor are associated with changes in expected mosquito counts while holding other variables constant at their average values. Values, where the spline and its confidence bands are above the blue line, indicate statistical significance and a positive association with \u003cem\u003eAe. aegypti\u003c/em\u003e abundance, for the respective values of the covariate, indicated on the x-axis. Conversely, spline functions that reach below the blue line indicate values (along the x-axis) for that covariate that are associated with lower \u003cem\u003eAe. aegypti\u003c/em\u003e abundance. For the interaction spline (A for latitude and longitude - and I for temperature and day of year), the three-dimensional association is viewed as a contour plot instead. For both, lighter values indicate areas on the contour plot associated with higher counts of \u003cem\u003eAe. aegypti \u003c/em\u003emosquitoes.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/b6068e39577194f8d46bbab3.png"},{"id":89847254,"identity":"ea019c26-4b80-472c-9e99-d2e6d2584f72","added_by":"auto","created_at":"2025-08-25 16:42:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2879537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/6f8c22fb-7247-42b3-81ba-97e6b61b0a6e.pdf"},{"id":80617409,"identity":"684c7aff-7ba5-4215-b0ad-ce3005f4e890","added_by":"auto","created_at":"2025-04-15 09:02:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1978576,"visible":true,"origin":"","legend":"","description":"","filename":"SanBernSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6280539/v1/34028c18d71cf6fb631fd5e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental correlates of Aedes aegypti abundance in the West Valley Region of San Bernardino County, California, U.S.A. from 2017- 2023: an ecological modeling study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eAedes\u003c/em\u003e mosquitoes (especially \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eAe. albopictus\u003c/em\u003e) are among the most important vectors of human disease globally.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e They are the major vectors for several epidemiologically important viruses, including dengue, Zika, and chikungunya viruses. In most regions, the primary public health approach to controlling \u003cem\u003eAedes\u003c/em\u003e-borne diseases is vector-focused. \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Understanding the ecology and spatio-temporal abundance of mosquito vectors can be important for targeting vector-focused interventions.\u003c/p\u003e \u003cp\u003eEnvironmental and meteorological factors have a major impact on the geographic distribution and abundance of mosquitoes. As ectotherms, ambient temperature has a non-linear association with mosquito activity, development, and population growth. \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Temperatures that are too cold or too hot can negatively impact mosquito development and population growth. \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Optimal temperatures can increase development and population growth.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe juvenile stages of mosquitoes are aquatic, meaning that water is crucial for mosquito habitation and population growth. Precipitation is therefore frequently associated with increased mosquito populations as it often leads to increased bodies of water that can serve as the necessary aquatic environment for the juvenile stages of mosquitoes. Other ecological and environmental factors are likewise important. \u003cem\u003eAe. aegypti\u003c/em\u003e is a container-breeding mosquito, preferring to lay eggs and spend its aquatic stages in human-made or naturally occurring containers that hold small bodies of water (e.g. cups, cans, old tires, bromeliads, tree holes). \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven an optimal temperature and the presence of preferential bodies of water, \u003cem\u003eAe. aegypti\u003c/em\u003e can thrive in urban and suburban settings. While this mosquito species is known to have relatively short flight ranges (normally shorter than 200m) it can disperse through human transportation routes, for example through cargo ships.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Once this species has invaded a geographic region with suitable temperature and aquatic habitats, it is difficult to control for a variety of reasons (e.g. many small cryptic habitats across a landscape can be difficult to find).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes have been detected in California since 2013, and their range and abundance appear to be expanding.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e By the end of 2023, the species had been detected in over 300 cities within 24 central and southern counties of the State.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Because of its vectorial capacity, urban-dwelling nature, and its anthropophilic behavior,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e it poses a significant risk of \u003cem\u003eAedes\u003c/em\u003e-borne disease transmission. The presence of this mosquito vector species locally breeding has alarmed vector control agencies about the possibility of establishment of local transmission of \u003cem\u003eAedes\u003c/em\u003e-borne disease. Indeed, such local transmission has already occurred in Florida,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and in 2024, 16 locally acquired dengue cases have been reported in southern California.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e With travel-related \u003cem\u003eAedes\u003c/em\u003e-borne diseases on the rise,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e the risk of importation and local transmission is high and growing.\u003c/p\u003e \u003cp\u003eThis study aims to examine granular spatial and temporal patterns of \u003cem\u003eAe. aegypti\u003c/em\u003e from 2017 to 2023 and to identify important environmental and meteorological factors associated with its abundance in the West Valley Mosquito and Vector Control District of San Bernardino County (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Since this species is of public health importance, an understanding of the factors that influence its geographic and temporal distribution is important for targeted mosquito control efforts.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the West Valley Mosquito and Vector Control District (WVMVCD) of southwestern San Bernardino County, encompassing six cities: Chino, Chino Hills, Ontario, Upland, Montclair and Rancho Cucamonga, with a total estimated population of 600,000 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The area is characterized by a Mediterranean climate, experiencing hot, dry summers and mild, wet winters.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Seasonal precipitation occurs predominantly between November and April, with occasional summer thunderstorms. Summer temperatures in the West Valley MVCD frequently surpass 38\u0026deg;C, with occasional spikes exceeding 42\u0026deg;C (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e \u0026amp; S2). The native vegetation includes chaparral, oak woodlands, and riparian habitats, although urbanization has led to significant alterations in the landscape.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Furthermore, the region has experienced rapid population growth in recent decades, accompanied by extensive urban development, including residential, commercial, and industrial structures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdult mosquito trapping\u003c/h3\u003e\n\u003cp\u003eWeekly mosquito surveillance was carried out by the West Valley Mosquito and Vector Control District (WVMVCD), a special district responsible for vector surveillance and control in the region. Mosquito collections took place from January 2017 to December 2023 using BG-Sentinel traps (Biogents AG, Regensburg, Germany), which were deployed weekly, totaling 6,794 trap nights (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). BG-Sentinel traps are recognized for their effectiveness in monitoring \u003cem\u003eAe. aegypti\u003c/em\u003e across spatial and temporal trends.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e To enhance mosquito attraction, the traps were equipped with BG-Lure (Biogents AG), an artificial human scent, and a bucket containing 1 kg of dry ice.\u003c/p\u003e \u003cp\u003eTrapping was systematically conducted throughout the district and set in residential areas, with the homeowner's consent traps were left overnight and mosquitoes were collected the next morning, and then counted and sorted by species. Each trap included a geographic location (latitude and longitude) and a timestamp indicating when it was set. Trap sites were strategically chosen based on historical mosquito hotspots, service requests from residents (e.g., complaints about mosquito biting), and an exploratory strategy to monitor mosquito activity across different areas of the district. All traps were brought to the WVMVCD Lab for mosquito counting and species identification by trained technicians. The District maintains records of adult mosquito counts, categorized by location and date, from 2017 to 2023\u003c/p\u003e\n\u003ch3\u003eEnvironmental and Meteorological Variables\u003c/h3\u003e\n\u003cp\u003eEnvironmental and meteorological data were obtained through the Google Earth Engine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) from a variety of Earth observation data sources.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e We extracted data on built environment, elevation, precipitation, normalized difference water index (NDWI, a measure of surface water or moisture), and average ambient temperature - all chosen for their hypothesized or known impact on mosquito abundance (listed in detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese data were downloaded and merged into the trapping dataset based on location and trap date. Buffers of 150m in size were drawn around each of the trap locations and environmental and meteorological variables were attributed to the traps based on these buffers. This buffer size was chosen based on the assumption that \u003cem\u003eAe. aegypti\u003c/em\u003e dispersal is generally limited to within 100 meters of their breeding sites.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In our exploratory analyses we also tested the use of 250m buffers, based on a study from central California that suggested this species may have broader dispersal in nearby areas.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe extracted precipitation, surface water, and average temperature with 14-day and 28-day lag periods from the mosquito collection date to account for the time needed for these environmental factors to influence mosquito development and population dynamics.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of variables included in the final model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDate Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Aedes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounts of adult \u003cem\u003eAe. aegypti\u003c/em\u003e per trap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWVMVCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJan, 1, 2017 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeteorological\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation (mm)\u003csup\u003e\u0026dagger;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog- transformation of mean precipitation per trap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopernicus Climate Change Service (C3S) ERA5-Land provides precipitation data with a resolution of 9 km.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDec, 1, 2016 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage. temperature (K) \u003csup\u003e\u0026dagger;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean maximum temperature in per trap in Celsius (based on buffer around trap)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopernicus Climate Change Service (C3S) ERA5-Land provides precipitation data with a resolution of 9 km.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDec, 1, 2016 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay of the year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay of the year on which the trap was checked (Day 1 was the first day of the study year and day 365 was the last)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJan, 1, 2017 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear of the study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface water \u003csup\u003e\u0026dagger;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean surface water index per trap (based on buffer around trap)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSentinel-2 MultiSpectral Instrument provides NDWI index value with a resolution of 30 meters. \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDec, 1, 2016 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEvery 5 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean elevation from buffer around trap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNASA Shuttle Radar Topography Mission Global 1 arc second (SRTMGL1) product, with a resolution of approximately 30 meters.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt area\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of pixels classified as\u003c/p\u003e \u003cp\u003e\u0026lsquo;built\u0026rsquo; within the 150m radius of each\u003c/p\u003e \u003cp\u003erespective trap per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynamic World provides near real-time (NRT) land use land cover (LULC) with a resolution of 10 meters.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYearly (taken from September)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude of each trap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWVMVCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJan, 1, 2015 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude of each trap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWVMVCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJan, 1, 2015 - Dec, 31, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e Mean centered and standardized.\u003c/p\u003e \u003cp\u003e*14 and 28 days lags\u003c/p\u003e \u003cp\u003eNA\u0026thinsp;=\u0026thinsp;Not applicable\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTemporal patterns in trapped\u003c/b\u003e \u003cb\u003eAedes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted descriptive statistics to examine counts of trapped \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes over time. We investigated counts of \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes per trap over years (investigating potential changes in abundance over years), as well as by month across all years (investigating potential seasonal patterns (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial patterns in trapped Aedes\u003c/h3\u003e\n\u003cp\u003eWe mapped counts of \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes per trap during different time periods (2017\u0026ndash;2020 and 2021\u0026ndash;2023) to investigate general shifts in trap yield across the study period. We then employed two main approaches to examine the potential spatial patterns of \u003cem\u003eAe. aegypti\u003c/em\u003e abundance from 2017 to 2023. First, we created spatial correlograms for each year to investigate how spatial autocorrelation in mosquito abundance changes with increasing distance between trap locations. These correlograms were constructed by calculating Moran's I at increasing distance intervals up to 3 km, using 500-meter bins. To account for sampling effort variations, we used the mean weekly number of mosquitoes per trap location within each year for these calculations. This approach provided insights into the scale of spatial dependence and its annual variations.\u003c/p\u003e \u003cp\u003eFollowing this global assessment, we employed the Getis-Ord Gi statistic to identify significant local clusters of high (hot spots) and low (cold spots) \u003cem\u003eAe. aegypti\u003c/em\u003e abundance. We used a distance-decay approach for the neighbor weights matrix, with a 500-meter threshold. Locations with statistically significant high positive Z-scores (\u0026gt;\u0026thinsp;1.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified as hot spots, while those with significantly low negative Z-scores (\u0026lt; -1.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were classified as cold spots.\u003c/p\u003e \u003cp\u003eTo ensure robustness, we conducted a sensitivity analysis by repeating the correlogram analysis and Getis-Ord Gi* using 250-meter bins/ threshold, comparing results with our primary analysis. We generated annual hotspot maps and created a combined visualization of annual spatial correlograms.\u003c/p\u003e \u003cp\u003eThis multi-approach method allowed us to track changes in spatial clustering patterns over time. Our exploratory spatial analyses provided a foundation for investigations into environmental correlates of \u003cem\u003eAe. aegypti\u003c/em\u003e abundance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBivariate Analysis between environmental and meteorological covariates and\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003eabundance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe hypothesized that the spatial and temporal distribution of \u003cem\u003eAe. aegypti\u003c/em\u003e in the WVMVCD was positively associated with precipitation, surface water, and built environment. We also hypothesized that higher ambient temperatures would be associated with higher mosquito abundance. To visualize potential associations between \u003cem\u003eAe. aegypti\u003c/em\u003e abundance and environmental factors, mosquito abundance data from traps were categorized into quartiles (Q1-Q4), where Q1 represents the lowest 25% of abundance counts and Q4 represents the highest 25%. We then plotted continuous environmental variables by mosquito abundance quartiles. This approach allowed for a preliminary examination of potential associations between each factor and \u003cem\u003eAe. aegypti\u003c/em\u003e abundance in our study area.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeneralized Additive Model for associations between environmental and meteorological covariates and\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003eabundance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe then used a negative binomial generalized additive model (GAM) to model counts of trapped \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes, using the aforementioned environmental variables as covariates in the model and accounting for trap location and time, for the duration of the 2017\u0026ndash;2023 study period. The GAM allowed us to account for the complex and potentially non-linear associations between environmental variables and \u003cem\u003eAe. aegypti\u003c/em\u003e abundance through the use of smoothing splines functions. \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo account for potential lagged effects (i.e. precipitation might have a delayed effect on mosquito abundance), we explored different lags (14-days and 28-days) for the covariates. We began by developing a model for all data from 2017 through 2023, with all covariates specified as thin plate regression spline functions. All covariates were centered on their means and standardized by their standard deviation to facilitate the interpretation and comparison of effect sizes.\u003c/p\u003e \u003cp\u003eFinal variable selection and model specification was based on a combination of scientific hypotheses, model goodness-of-fit tests (deviance explained and the Akaike Information Criterion (AIC)), and interpretability. Through comparison of model fit using Deviance Explained and Akaike Information Criterion (AIC), we determined that a 14-day lag for these variables provided the best model fit. The final negative binomial GAM included the following variables: NDWI, precipitation, built environment, elevation, average temperature, geographic location, season, and year. It also incorporated interaction terms between season and average temperature, as the effect of temperature shifted over time.\u003c/p\u003e \u003cp\u003eThe results of our regression analysis are presented as graphs showing the estimated smoothed spline functions for each predictor variable, along with a summary table indicating statistical significance.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSoftware\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using the R statistical software (version 4.3.3). The spatial autocorrelation analyses were run using spdep and tmap packages. The GAM was created using the mgcv package. All maps were created using QGIS (version 3.34.9).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eTemporal distribution of trapped\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003emosquitoes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe abundance of \u003cem\u003eAe. aegypti\u003c/em\u003e has increased in this setting over the study period (2017\u0026ndash;2023), even while accounting for increased surveillance and trapping. While only 358 adult \u003cem\u003eAe. aegypti\u003c/em\u003e were trapped in 2017; 2,954 were trapped in 2019 and 30,147 were trapped in 2023. While trapping and surveillance increased from 682 traps set in 2017 to 1,538 in 2023, the mean and median numbers of \u003cem\u003eAe. aegypti\u003c/em\u003e caught per trap night increased from 0.52 and 0 (mean and median respectively) per trap in 2017 to 19.6 and 7 per trap (mean and median respectively) in 2023. The proportion of traps set that did not catch any mosquitoes also decreased over this period, from 91% in 2017 to 23% in 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere was a clear seasonal pattern in trapped adult \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes as well (Table S3). August, September, and October had the highest numbers of trapped mosquitoes (higher mean per trap, higher median per trap, and lower proportion of traps catching zero adult \u003cem\u003eAe. aegypti\u003c/em\u003e). This time period roughly corresponds to the local dry and hot season (Supplemental Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics for trapped adult \u003cem\u003eAe. aegypti\u003c/em\u003e and traps by year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal \u003cem\u003eAe. aegypti\u003c/em\u003e caught\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of traps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean per trap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esd per trap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian per trap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIQR per trap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProportion of traps with no mosquitoes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial patterns in trapped\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003emosquitoes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe increase in abundance of trapped \u003cem\u003eAe. aegypti\u003c/em\u003e in the WVMVCD during the study period (2017\u0026ndash;2023) was likewise evidenced in maps of trapped mosquitoes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Trapping and surveillance have expanded at the same time, but this does not account for the increase in abundance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpatial correlograms for \u003cem\u003eAe. aegypti\u003c/em\u003e from 2017 through 2023 indicate positive spatial autocorrelation (measured using Moran's I statistic) at relatively small distances (especially\u0026thinsp;\u0026lt;\u0026thinsp;500 meters) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All but one year (2018) showed positive clustering up to approximately 1km, and four years had clustering at 500m or less. No clear and consistent pattern of clustering was apparent at distances larger than 1km.\u003c/p\u003e \u003cp\u003eAdditionally, our spatial analysis using the Getis-Ord Gi* statistics with a 500-meter bin revealed dynamic patterns in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance from 2017 to 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the early years, hot spots were concentrated in the south (i.e. near Chino Hills State Park and residential areas). Over time, these clusters shifted and new hot spots emerged in western, central, and northern parts of the study area, encompassing diverse land use types including parks, residential, and business zones. Notably, the Chino Hills State Park hotspot persisted for two years and then transitioned from a hot spot to a cold spot by 2019. While some hot spots showed temporal stability from 2020 to 2022, several became cold spots by 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelations between meteorological and environmental variables and\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003eabundance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe bivariate analysis indicated positive associations between temperature, seasonality, and recent years with higher mosquito abundance quartiles, while elevation showed a negative association (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Precipitation displayed a complex pattern, and surface water and built area showed no clear relationships across the abundance quartiles (Q1\u0026ndash;Q4).\u003c/p\u003e \u003cp\u003eTemperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) showed a positive association with mosquito abundance, with higher abundance quartiles (Q3 and Q4) corresponding to higher average temperatures. This pattern was similar to the observed seasonality (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), where higher abundance quartiles clustered around mid-year. Temporal trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) were observed, with earlier years (2020\u0026ndash;2022) correlating with higher abundance quartiles.\u003c/p\u003e \u003cp\u003ePrecipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) exhibited a complex relationship with abundance. All quartiles displayed consistently low median values, while higher abundance quartiles showed greater skewness and outliers. NDWI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and built area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) displayed minimal variation across the abundance quartiles, showing no clear association with mosquito abundance in this context. Elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG) demonstrated a slight negative association with abundance, with higher abundance quartiles occurring at slightly lower elevations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults from the negative binomial GAM for environmental and meteorological correlates of\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003eabundance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe spline interaction term for latitude and longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) is a representation of the association between location and abundance of \u003cem\u003eAe. aegypti\u003c/em\u003e, after accounting for the other covariates in the model. This map demonstrates the clustering of mosquito abundance in the south and central-east parts of the study area, as indicated by the bright white and yellow areas on the map.\u003c/p\u003e \u003cp\u003eSurface water (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) and average temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF) showed positive associations with \u003cem\u003eAe. aegypti\u003c/em\u003e abundance. There was a positive association with elevation at low to mean levels of the variable, but all higher elevation areas showed no statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC - confidence bands on both sides of the 0 point on the y-axis beginning at roughly 0.5 standard deviations above mean elevation and extending to all higher elevations). The model showed no association between the built environment (\"Urban area\" Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) and mosquito abundance, or with precipitation and \u003cem\u003eAe. aegypti\u003c/em\u003e abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eA strong seasonal pattern was evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), with higher abundance occurring during August, September, and October of each year. Furthermore, the general increase in abundance over time (years) was apparent in the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH) indicating an increase in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance over the study period (2017\u0026ndash;2023), even after accounting for the other covariates in the model.\u003c/p\u003e \u003cp\u003eFinally, the interaction between average temperature and day of the year (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI) revealed that temperature affects \u003cem\u003eAe. aegypti\u003c/em\u003e abundance varied throughout the year. In early spring (March-April), lower temperatures corresponded with lower mosquito counts. During the summer months (May-September), higher temperatures were associated with increased abundance. This relationship extended into early autumn, with elevated counts persisting for a short period after temperatures began to drop in October. Temperature ranges varied significantly across seasons, with lower temperatures during peak summer months (July and August) exceeding the highest temperatures observed during the coldest months (December and January).\u003c/p\u003e \u003cp\u003eWe ran a sensitivity analysis using 28-day lags for the time-varying covariates (see Table S3 and Figure S3). The model was largely the same with the exception that the ambient temperature from 28 days before a trap date was not a significant predictor of mosquito abundance (whereas in the main model presented here, ambient temperature 14 days before trapping was a significant predictor).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eOur study on \u003cem\u003eAe. aegypti\u003c/em\u003e mosquito abundance from 2017 to 2023 in the WVMVCD (part of San Bernardino County) in southern California revealed several key findings. We observed a significant increase in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance over the study period, from 0.52 per trap in 2017 to 19.60 in 2023, with yearly increases corresponding to summer months. We found that surface water, temperature, seasonality, and geographic location were predictors associated with mosquito abundance. Additionally, spatial clustering of high mosquito abundance was concentrated in the central and southern regions of the study area - though clusters did shift over time.\u003c/p\u003e \u003cp\u003eIn our analysis, surface water was associated with \u003cem\u003eAe. aegypti\u003c/em\u003e abundance. Conversely, precipitation was not associated with abundance. The region is semi-arid with very little precipitation in an average year. The lack of a clear relationship between precipitation and \u003cem\u003eAe. aegypti\u003c/em\u003e abundance could be explained by the ecology of this species. This mosquito often breeds in small artificial containers located around human dwellings, meaning seasonal patterns in population dynamics might be more influenced by variations in peridomestic water availability than changes in precipitation. Heavy rains could likewise have a negative effect on larval abundance, since intense rainfall may flush the larval habitat, thereby affecting the adult mosquito population.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e We also note that in this setting, surface water does not come from precipitation alone since patterns in surface water over time did not align with precipitation. We hypothesize that much of this surface water is from residential yard watering and agricultural irrigation, which increases during prolonged periods of the dry season without precipitation. While surface water availability may serve as a potential predictor of \u003cem\u003eAe. aegypti\u003c/em\u003e distribution, its influence is dependent on additional environmental and anthropogenic factors.\u003c/p\u003e \u003cp\u003eWe found that average temperature was also an important factor with regard to the abundance of \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes. In our model, average temperature significantly influences \u003cem\u003eAe. aegypti\u003c/em\u003e abundance, but it is important to note that seasonal factors, including photoperiod, could modulate the effect of temperature on mosquitoes. The interaction between temperature and season highlights the complexity of mosquito population dynamics, where optimal conditions depend on multiple environmental factors.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e While warmer temperatures during peak season generally promote mosquito development, extreme heat can reduce habitat suitability. Longer daylight hours in summer enhance mosquito activity and reproduction, whereas shorter daylight hours in cooler months suppress it, even when temperatures remain favorable.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e These findings emphasize the need to consider both temperature and seasonal cues in predicting mosquito populations to improve vector control strategies.\u003c/p\u003e \u003cp\u003eGeographic location plays a crucial role in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We did not find a significant association between built area and mosquito abundance in our model, likely because most of the traps were set at land use categorized as urbanized. However, using spatial analysis we identified localized spatial clustering of \u003cem\u003eAe. aegypti\u003c/em\u003e abundance in various locations across the study area. For example, the spatial correlogram analysis showed that \u003cem\u003eAe. aegypti\u003c/em\u003e populations are highly localized and the Getis-Ord analysis revealed spatial-temporal shifts in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance, with initial hotspots concentrated in the southern part of the study area in 2017 gradually expanding and relocating to central and northern regions in subsequent years. Our regression analyses suggest that some of these spatial and temporal patterns are driven by micro-environmental factors such as water availability, ambient temperature, and human activity.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMany of these hot spots were observed in residential areas and near public spaces such as parks and schools, likely due to the presence of suitable breeding sites and human hosts. Clusters of high mosquito abundance were also noted in proximity to commercial areas, including real estate developments, fire sprinkler system businesses, junk removal services, and major roads (e.g., Highways 210 and 110). Cold spots, while less frequent, were observed in some locations, including near a senior center. The distribution of both hot spots and cold spots varied throughout the study period, with some areas showing persistent patterns and others demonstrating changes over time.\u003c/p\u003e \u003cp\u003eThe spatial and temporal variability in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance has important implications for vector control strategies. Persistent hotspots may require targeted, intensive interventions, while areas with fluctuating patterns suggest the need for adaptive, flexible approaches. Understanding these dynamics is critical for optimizing resource allocation in mosquito control efforts, whether through traditional methods, novel techniques like sterile insect release, or integrated vector management strategies. This knowledge can guide decision-makers in prioritizing high-risk areas, adjusting intervention timing and intensity, and potentially improving the cost-effectiveness of control measures. Ultimately, these insights contribute to more efficient and sustainable vector management, which is critical for reducing the risk of mosquito-borne diseases in urban environments.\u003c/p\u003e \u003cp\u003eThe spatial and temporal patterns we observed in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance reflects the complex dynamics of this species' expansion into new environments. \u003cem\u003eAe. aegypti\u003c/em\u003e has been expanding in southwestern parts of the United States in recent years,\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e a trend of particular concern given the large population in California, the relatively frequent travel-related \u003cem\u003eAedes-\u003c/em\u003eborne diseases, and the recent occurrence of locally transmitted dengue in the region.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The relationships that we observed, which partially differ from what is normally seen in other tropical and subtropical regions of the world where the mosquito is endemic (and normally more common in the rainy season), highlight potential challenges for public health and vector control agencies. Heightened surveillance that includes ecological research into the environmental, meteorological, and geographic correlates of \u003cem\u003eAe. aegypti\u003c/em\u003e abundance is important for understanding seasonal, interannual, and geographic patterns in abundance. As this species moves into new environments, identifying consistent environmental predictors of its presence and abundance may remain challenging until it reaches equilibrium with local environmental conditions.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Strengths\u003c/h2\u003e \u003cp\u003eThis study has both limitations and strengths. Our assessment of mosquito abundance is limited to locations and time periods when trapping occurred (February to November). Over time, trapping efforts expanded geographically, coinciding with an increase in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance. While our model accounted for trapping intensity, we were unable to assess abundance in areas and time periods without trapping. Additionally, trapping itself may influence \u003cem\u003eAe. aegypti\u003c/em\u003e abundance, as mosquitoes removed from the environment are not replaced immediately.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Nonetheless, we believe the trapped mosquitoes provide a reliable representation of abundance in the surrounding environment. Our analyses incorporated Earth observation data, primarily derived from satellite imagery. These data can be affected by cloud cover, potentially leading to incomplete surface water measurements.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e However, unlike tropical regions where cloud cover can persist for weeks or months, this issue is relatively minor in this arid part of North America. There were relatively few periods during the study when Earth observation data were unavailable.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study has several notable strengths. Earth observation sensors (e.g., satellite systems) enabled remote monitoring of environmental attributes at fine spatial and temporal resolutions throughout most of the study period.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Comparable on-the-ground measurements would have been cost-prohibitive and labor-intensive, making long-term monitoring impractical. As more Earth observation data become available, their integration into vector-borne disease surveillance in this and other settings will likely expand. \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Moreover, the extensive surveillance data collected over eight years represent a major strength of this study. The richness of these spatial and temporal data enabled a robust analysis of mosquito distribution patterns, revealing both long-term trends and localized variations.\u003c/p\u003e \u003cp\u003eFinally, as the threat of \u003cem\u003eAedes\u003c/em\u003e-borne diseases in this region increases vector control agencies have moved toward the implementation of different novel interventions.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e In the West Valley MVCD, sterile insect release is currently being incorporated into vector control efforts.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e A recent work indicated a reduction in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance by up to 65% following the optimization of integrated vector management strategies through adoption of target sterile insect technique and In2Care Mosquito Stations in selected sites in the area in 2024.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e The model presented here, which accounts for environmental factors influencing \u003cem\u003eAe. aegypti\u003c/em\u003e abundance, can be adapted to assess the relative impact of this intervention while accounting for spatial and temporal variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights a significant increase in \u003cem\u003eAe. aegypti\u003c/em\u003e abundance in West Valley MVCD of San Bernardino County over the past eight years, and highlights complex relationships between environmental, meteorological, and human-driven factors and their abundance. Our findings emphasize the importance of surface water\u0026mdash;potentially driven by human activities such as garden irrigation\u0026mdash;alongside temperature and seasonality as key predictors of mosquito abundance in this arid region. The integration of long-term surveillance data with satellite-derived environmental metrics provides a strong foundation for ongoing research and monitoring efforts, which will be important as the region is now incorporating innovative vector control approaches such as sterile insect release. Accurately modeling the relative impact of such interventions while accounting for geographic and environmental drivers of abundance will be essential. Finally, as locally transmitted \u003cem\u003eAedes\u003c/em\u003e-borne diseases continue to emerge in North America\u0026mdash;including recent cases in Southern California\u0026mdash;understanding spatial and temporal shifts in mosquito abundance will be essential for public engagement, vector control, and disease prevention efforts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this manuscript can be requested by contacting the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the West Valley Mosquito and Vector Control District for the provision of the historical mosquito surveillance data.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge funding support from the Pacific Southwest Regional Center of Excellence for Vector-Borne Diseases funded by the U.S. Centers for Disease Control and Prevention (Cooperative Agreement 1U01CK000649).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003cbr\u003e\u003c/strong\u003eThis research was funded by the Pacific Southwest Regional Center of Excellence for Vector-Borne Diseases and the U.S. Centers for Disease Control and Prevention. (Cooperative Agreement 1U01CK000649).\u0026nbsp;\u003cbr\u003e\u0026nbsp;The funders played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeta S, Beyene TJ, De Clercq EM, Amenu K, Kraemer MUG, Revie CW. Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. 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Published online August 20, 2024:tjae106. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jme/tjae106\u003c/span\u003e\u003cspan address=\"10.1093/jme/tjae106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirhanie SK, Hans J, Castellon JT, et al. Reduction in Aedes aegypti Population After a Year-Long Application of Targeted Sterile Insect Releases in the West Valley Region of Southern California. Insects. 2025;16(1):81. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/insects16010081\u003c/span\u003e\u003cspan address=\"10.3390/insects16010081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6280539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6280539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eAedes\u003c/em\u003e mosquitoes, particularly \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eAe. albopictus\u003c/em\u003e, are major vectors of globally significant diseases such as dengue, Zika, and chikungunya. Since 2013, \u003cem\u003eAe. aegypti\u003c/em\u003e populations have rapidly expanded in California, making control efforts difficult because of their cryptic breeding sites and urban habitat preference. Remote sensing technologies, coupled with Geographic Information Systems (GIS), offer innovative solutions for mosquito surveillance and control. However, understanding the environmental drivers of mosquito abundance, particularly in California’s diverse ecological settings, remains a critical gap. To address this gap, we analyzed \u003cem\u003eAe. aegypti\u003c/em\u003e abundance (2017 to 2023) in relation to environmental variables, such as temperature, precipitation, surface water, elevation, and built environment. We applied hotspot analysis to identify spatial clusters of high mosquito abundance and used a generalized additive model (GAM) with a negative binomial distribution to assess environmental and meteorological influences on mosquito counts. Hotspot analyses revealed clusters of \u003cem\u003eAe. aegypti\u003c/em\u003e hotspots near residential areas. \u003cem\u003eAe. aegypti\u003c/em\u003e counts increased with higher surface water availability and temperature. Our study elucidates the complex dynamics of \u003cem\u003eAe. aegypti\u003c/em\u003e mosquito abundance in the West Valley Region of San Bernardino County from 2017 to 2023, shedding light on the influence of environmental factors and human activities on temporal trends. Our findings emphasize the critical role of temperature and water availability in shaping mosquito population dynamics, highlighting the need for proactive vector control strategies in response to environmental changes.\u003c/p\u003e","manuscriptTitle":"Environmental correlates of Aedes aegypti abundance in the West Valley Region of San Bernardino County, California, U.S.A. from 2017- 2023: an ecological modeling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 08:53:55","doi":"10.21203/rs.3.rs-6280539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-27T11:55:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-26T17:42:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T22:16:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141342905767849159808585949557235643489","date":"2025-03-29T17:00:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297252447858720869969830629325359203694","date":"2025-03-27T15:33:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T15:28:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-25T15:12:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T14:18:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2025-03-21T22:57:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55fa6edb-f5d5-4c44-b489-6eefdb959e4a","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:36:00+00:00","versionOfRecord":{"articleIdentity":"rs-6280539","link":"https://doi.org/10.1186/s13071-025-06967-w","journal":{"identity":"parasites-and-vectors","isVorOnly":false,"title":"Parasites \u0026 Vectors"},"publishedOn":"2025-08-18 16:29:31","publishedOnDateReadable":"August 18th, 2025"},"versionCreatedAt":"2025-04-15 08:53:55","video":"","vorDoi":"10.1186/s13071-025-06967-w","vorDoiUrl":"https://doi.org/10.1186/s13071-025-06967-w","workflowStages":[]},"version":"v1","identity":"rs-6280539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6280539","identity":"rs-6280539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}