
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39300158
72734
10.1038/s41598-024-72734-z
Article
Effects of Hurricane Irma on mosquito abundance and species composition in a metropolitan Gulf coastal city, 2016–2018
Moise Imelda K. moise@miami.edu

1
Huang Qian 1
Mutebi John-Paul 2
Petrie William D. 2
1 https://ror.org/02dgjyy92 grid.26790.3a 0000 0004 1936 8606 Department of Geography, University of Miami, 1300 Campo Sano Ave, Coral Gables, FL 33124 USA
2 grid.421336.1 0000 0000 8565 4433 Miami-Dade County Mosquito Control Division, Miami, FL USA
19 9 2024
19 9 2024
2024
14 2188631 3 2024
10 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Mosquitoes are the most common disease vectors worldwide. In coastal cities, the spread, activity, and longevity of vector mosquitoes are influenced by environmental factors such as temperature, humidity, and rainfall, which affect their geographic distribution, biting rates, and lifespan. We examined mosquito abundance and species composition before and after Hurricane Irma in Miami, Dade County, Florida, and identified which mosquito species predominated post-Hurricane Irma. Our results showed that mosquito populations increased post-Hurricane Irma: 7.3 and 8.0 times more mosquitoes were captured in 2017 than at baseline, 2016 and 2018 respectively. Warmer temperatures accelerated larval development, resulting in faster emergence of adult mosquitoes. In BG-Sentinel traps, primary species like Ae. tortills, Cx. nigripalpus, and Cx. quinquefasciatus dominated the post-Hurricane Irma period. Secondary vectors that dominated post-Hurricane Irma include An. atropos, An. crucians, An. quadrimaculatus, Cx. erraticus, and Ps. columbiae. After Hurricane Irma, the surge in mosquito populations in Miami, Florida heightened disease risk. To mitigate and prevent future risks, we must enhance surveillance, raise public awareness, and implement targeted vector control measures.

Keywords

Flooding
Vector-borne disease
United States
Rainfall
And disease
Surveillance
Subject terms

Environmental social sciences
Natural hazards
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Despite decades of coordinated mosquito control efforts ranging from mechanical, chemical to integrated mosquito control in the U.S. and internationally, mosquitoes remain the most common disease vectors globally1–4. Mosquitoes are responsible for spreading a wide range of pathogens of public health importance such as plasmodium falciparum, dengue, Zika and West Nile viruses, that can have severe and life-threatening consequences (e.g., encephalitis, meningitis, hemorrhagic fever, and microcephaly)5–7. Different risk factors influence the resurgence and spread of mosquito-borne diseases, mosquito abundance, species richness, and abundance of susceptible vertebrates: meteorological factors (e.g., temperature, relative humidity, and precipitation), urbanization, sociodemographic factors, ecology, and changes in public health policy8–15. In coastal cities, recent studies have indeed linked the spread, activity, and longevity of vector mosquitoes to climatic changes16. These changes can have significant implications for mosquito population dynamics, disease transmission, and public health preparedness. Climate change, including rising temperatures, altered precipitation patterns, and sea level rise, affects mosquito habitats, breeding sites, and behavior11,17,18,19 As a result, coastal cities face increased risks related to mosquito-borne diseases, such as dengue, Zika, and West Nile (WNV)20–24. Understanding these connections is crucial for effective mosquito control strategies and disaster response planning25–32.

Hurricanes bring significant abiotic changes to mosquito populations11,26,33. Favorable effects include increased breeding sites due to heavy rainfall, nutrient enrichment in floodwaters, warmer temperatures, and elevated humidity34,35. However, unfavorable changes include habitat disruption and salinity fluctuations36. Understanding this complex interplay is crucial for effective mosquito control and public health preparedness.

Previous studies in the U.S. have documented important relationships between mosquito activity and post hurricane events (increases in the mosquito Culex quinquefasciatus in New Orleans, Louisiana following Hurricane Katrina)11 and increases in Aedes aegypti populations (e.g., five weeks after Hurricane Maria in Puerto Rico)37. Similar results were reported in Collier County, Florida, where daily landing rates of 150 adult females/two-minute period were observed for the black salt-marsh mosquito Aedes taeniorhynchus following Hurricane Irma but decreased a year later38. In Texas, Cx. nigripalpus increased following Hurricane Harvey39. Weather-related factors such as temperature, relative humidity and precipitation were reported to influence mosquito seasonal activity, as they can alter mosquito development and reproductive and mortality rates40–42. However, elsewhere (Louisiana), the results of mosquito activity in the context of hurricanes and storm events have been mixed43, with some studies conducted after Hurricane Katrina in Louisiana and Mississippi finding increases in the number of reported cases of mosquito-borne disease (West Nile neuroinvasive disease) compared to previous years44 and an increase in the average total number of infected mosquitoes (specifically WNV) on the northern coast of the Gulf of Mexico36. The findings of other studies contradict these findings43 or revealed no arboviruses in mosquitoes collected six weeks after Hurricane Katrina34.

A recent global scoping review on flooding events and mosquito-borne diseases noted that although flooding is linked to an increased incidence of mosquito-borne diseases (dengue and malaria), the results were mixed regarding lag periods, with studies focused on dengue reporting short-term (less than a month) decreases and subsequent (1–4 month) increases in incidence, while those focused on malaria noted post event incidence increases; however, the results were mixed, and the temporal pattern was less clear45. Notably, such methodological inconsistencies limit direct comparison and generalizability of the study results.

Although the mosquito-borne diseases compared here are vectored by different mosquitoes with unique ecology and caused by entirely different pathogens (virus vs Protozoa), in the hurricane-prone state of Florida, mosquito-borne arboviruses (WNV, Saint Louis encephalitis virus, and Eastern equine encephalitis virus), and increases in the mosquito population have been reported post storm, posing a major threat to public health38,46–48. Notably, of the 292 hurricanes of which landfall occurred in the continental U.S. between 1851 and 2018, 123 (41%) struck Florida49, and projections show an increasing number of major hurricanes globally50, suggesting that coastal cities such as Miami, Florida, are at heightened risk from vector and nuisance mosquitoes. Moreover, rising sea levels may combine with local climate factors (e.g., temperature, rainfall, and wind) to increase the transmission of mosquito pathogens-causing diseases17 by influencing their vectors51,52.

Notably, the potential effect of rising sea levels on the prevalence of vector-borne infectious diseases has been documented by Ramasamy and Surendran17,52. Briefly, global climate change, exacerbated by rising sea levels, poses a significant risk to the transmission of mosquito-borne diseases in coastal zones. For example, as saline and brackish water bodies expand due to warming oceans, salinity-tolerant mosquitoes thrive, while freshwater mosquito vectors adapt to the changing environment. Therefore, monitoring disease incidence and developing preventive strategies are crucial to mitigate these risks. Furthermore, extreme climate events such as floods may increase the risk of vector-borne disease spillover53. Despite this, the potential impact of hurricanes on mosquito species composition or the window of lag time required for potential vector populations to develop and recover post-storm in Miami-Dade County is yet to be determined.

To address this gap in knowledge, first, we assessed adult mosquito populations (all species) before and after Hurricane Irma to understand the impact of storm events on mosquito abundance and estimated the time lags required for population development and recovery. Second, we determined which mosquito species predominated in the aftermath of Hurricane Irma. This information is crucial for understanding vector populations and their dynamics following extreme weather events. Third, we evaluated mosquito species composition using different trap types (e.g., light traps, gravid traps, CO₂ traps). By comparing trap-specific captures, we provide insights into species preferences and behavior during storm-induced disturbances in Miami-Dade County, Florida.

Methods

Study area

Miami-Dade County is in the southeastern part of the State of Florida and is the southeasternmost county on the U.S. mainland, between longitude − 80° 11′ 22.20" W and latitude 25° 46′ 16.19" N. Approximately 56% of Miami-Dade County’s land is 6 feet or less above sea level (this county is commonly called Miami, Miami-Dade, Dade County, Dade, Metro-Dade, or Greater Miami). Miami-Dade County is the third largest county in Florida, it has a surface area of 5,040 km49 (1,946 mi49). The average annual rainfall in the county is 61 inches of rain per year (7 inches more than the annual average in Florida), and the maximum and minimum average monthly temperatures are 90.6 °F and 41.8 °F, respectively. Peak rainfall generally occurs from mid-May through early October. In 2022, 12% of Florida’s total population were home to Miami-Dade County (2,701,762 population), which is one of the largest and most ethnically diverse yet poorest counties in the US11. The county was the epicenter of the 2016 Zika outbreak13, and cases of locally transmitted human cases of West Nile virus and dengue virus were diagnosed as recently as 202354,55.

Data sources

This was a retrospective observational study involving mosquito surveillance data and meteorological, socioeconomic, and demographic data. Table 1 presents the data variables of interest, spatial and temporal scales, and corresponding processing.Table 1 Summary of the input data considered for inclusion, Miami-Dade County, Florida.

Variables	Year	Original temporal resolution	Spatial resolution	Temporal aggregation method	
Mosquito counts	2016–2018	Trap level daily captures	Points	Epi-week	
Evacuation zone	2017	shapefile	boundaries	–	
Meteorological	2017	Weekly mean rainfall and temperature	Average epi-week mean rainfall and temperature	Epi-week	
Socioeconomic and demographic	2017	Census tract	Census tract	5-year estimates	
Mosquito data from two trap types, CDC light traps (n = 30 traps) and BG sentinel traps (n = 131 traps). The socioeconomic and demographic variables included population density, Hispanic percentage, African American percentage, sex, median household income, education attainment (at least a bachelor’s degree), and unemployment rate. Meteorological data.

The data included rainfall and temperature.

Mosquito data collection

Adult mosquito surveillance data from 2016 to 2018 were provided by the Miami-Dade County Mosquito Control and Habitat Management Division or MDC-MCHMD (from August 15, 2016, to October 15, 2018). The MDC-MCHMD uses two trap types; CDC light traps (John W. Hock Company, Gainesville, FL, USA) located at 30 sites and baited with dry ice and Biogents Sentinel (BG-Sentinel) traps located at 131 trap sites (Fig. 1). CDC light traps are sparsely and permanently located in remote and wooded areas across the county, while BG-Sentinel traps are emptied and rotated among the sampling sites. While trapping in CDC light traps has a long history in the county (established in 2013), the analysis was constrained to mosquito capture data from 2016 to 2018 to align with mosquito data collected in BG Sentinel traps (established in 2016 after the Zika outbreak) in the study area. The collected mosquitoes were processed and morphologically identified to species at the MDC-MCHMD laboratory using taxonomic keys56.Fig. 1 The spatial distribution of mosquito trap locations by trap type: (a) gravid traps and (b) light traps in Miami-Dade County, Florida. 61

Source: Miami-Dade Mosquito Control and Habitat Management Division, MDC, Florida, 2018. Map generated by the first author using ArcGIS software version 10.31

Study period and epidemiological weeks

We used weekly mosquito data collected from active mosquito trap types (CDC light traps and BG-Sentinel traps) collected within 24 h from Monday to Tuesday every week between 2016 and 2018. Because one objective of this study was to compare mosquito species composition and relative abundance pre- and post-Hurricane Irma in Miami-Dade County, we aggregated mosquito counts according to CDC week or epidemiological week (epi-week), a standardized method of counting weeks, to allow for comparisons of data year after year57. Since epi-weeks change annually and because Hurricane Irma struck Miami-Dade County on September 10, 2017, as a Category 3 storm, the mosquito data used in the analysis were further constrained to 8 epi-weeks.

The first four epi-weeks comprised the pre-hurricane Irma period corresponding to the dates August 15 to September 6, 2016, and the post-hurricane Irma period, which comprised the dates September 19 to October 15, 2018 (Table 2). Epi-week 37 or the week corresponding to Sep 12–18 in each study year was excluded from the analysis because this week denotes Hurricane Irma’s landfall in Miami-Dade County, and traps were temporarily taken down during that week. Mosquito data for 2016 and 2018 were used as baselines.Table 2 Number of active mosquito traps during the study period (epi-week 33–41; August 15, 2016-Otober 2018), Miami-Dade County, Florida.

Year	Trap Type	Pre-Hurricane Irma—4 Epi-weeks	Hurricane Period	Post-Hurricane Irma—4 Epi-weeks	
33
(Aug 15–21)	34
Aug 22–28)	35
(Aug 28-Sep 4)	36
(Sep 5–11)	37
Sep 12–18)	38
Sep 19–25)	39
(Sep 26-Oct 2)	40
(Oct 3–9)	41
(Oct 10–16)	
2018	BG-Sentinel	148	148	148	148	147	147	148	148	147	
CDC Light	30	30	30	30	30	30	30	30	30	
2016	BG-Sentinel	35*	35*	35*	90	91	88	88	55	97	
CDC Light	17	18	18	25	25	25	25	25	23	
2017	BG-Sentinel	131	131	131	96	Irma**	131	131	131	131	
CDC Light	30	30	30	30	28	28	29	30	
Excluded in the analysis are data from 35 BG-Sentinel traps that were used during the three “hot zone” neighborhoods during the 2016 Zika virus outbreak (Wynwood, Miami Beach, and Little River area and capturing Epi Week 33–35) because before this date, county did not operate BG-Sentinel traps. Also excluded was September 7–14, 2017 (Epi Week 37), which reflects the Hurricane Irma period. *35 depicts BG-Sentinel traps that are under the Domestic Surveillance Array (DSA) Program in rural areas in Miami-Dade County. **No mosquitoes were captured during the Hurricane Irma period.

Significant differences indicate in bold font.

The number of active traps used was divided by the number of mosquitoes (count) by the traps count to yield the mosquito/trap/night (Table 3). This data collection process yielded 609 trap nights pre-Hurricane Irma (n = 489 trap nights for BG-Sentinel traps and n = 120 trap nights for CDC light traps) and post-Hurricane Irma; 639 trap nights were estimated (n = 524 trap nights for BG-Sentinel traps and n = 115 trap nights for CDC light traps).Table 3 Total mosquito captures per trap type and trap night, Miami-Dade County, Florida.

Year	Trap Type	Description	Before Hurricane Irma		After Hurricane Irma	
Epi-week 33
(15-Aug)	Epi-week 34
(22-Aug)	Epi -week 35
(29-Aug)	Epi-week 36
(6-Sep)	Trap Nights (n)	Epi-week 37
(13-Sep)	Epi Week 38
(19-Sep)	Epi-week 39
(26-Sep)	Epi-week
40
(3-Oct)	Epi Week 41
(11-Oct)	Trap Nights (n)	
2017	BG- Sentinel	Mosquitoes (count)	3,944	949	1,971	1,349		Irma	6056	2,928	3,525	5,963		
Traps (count)	131	131	131	96	489	Irma	131	131	131	131	524	
Mosquito/night/trap	30.1	7.2	15.0	14.1		Irma	46.2	22.4	26.9	45.5		
CDC	Mosquitoes (count)	13,741	2,529	3,637	1,7473		Irma	38,898	3,3528	109,693	160,658		
Traps (count)	30	30	30	30	120	Irma	28	28	29	30	115	
Mosquito/night/trap	458	84	121	582		Irma	1,389	1,197	3,783	5,355		
(Light & BG, n)						609						639	
2016	BG- Sentinel	Mosquitoes (count)	Missing	Missing	Missing	1,371		1,543	1,868	1,762	978	1,617		
Traps (count)	Missing	Missing	Missing	90	90	91	88	88	55	97	328	
Mosquito/night/trap	Missing	Missing	Missing	15.2		17.0	21.2	20.0	17.8	16.7		
CDC	Mosquitoes (count)	2,500	1,371	1,078	13,006		9,092	4,339	3,438	14,650	9,072		
Traps (count)	17	18	18	25	78	25	25	25	25	23	98	
Mosquito/night/trap	147.1	76.2	59.9	520.2		363.7	173.6	137.5	586.0	394.4		
(Light & BG, n)						168						426	
2018	BG- Sentinel	Mosquitoes (count)	2,539	2,891	1,706	2,668		3,591	2,688	1,804	2,023	1,907		
Traps (count)	148	148	148	148	592	147	147	148	148	147	590	
Mosquito/night/trap	17.2	19.5	11.5	18.0		24.4	18.3	12.2	13.7	13.0		
CDC	Mosquitoes	952	2,875	2,605	3,336		16,470	9,499	6,120	3,837	3,567		
Traps (count)	30	30	30	30	120	30	30	30	30	30	120	
Mosquito/night/trap	31.7	95.8	86.8	111.2		549.0	316.6	204.0	127.9	118.9		
Light & BG, n						712							
2013	CDC	Mosquitoes (count)	1,506	1,456	1,560	2,118		2,665	3,360	4,205	8,377	2,050		
Traps (count)	30	28	26	24	108	27	28	28	27	25	108	
Mosquito/night/trap	50.2	52.0	60.0	88.3		98.7	120.0	150.2	310.3	82.0		
2014	CDC	Mosquitoes (count)	17,461	7,713	3,169	2,097		7,634	2,621	7,724	15,352	15,752		
Traps (count)	29	29	28	28	114	28	27	28	29	28	112	
Mosquito/night/trap	602.1	266.0	113.2	74.9		272.6	97.1	275.9	529.4	562.6		
2015	CDC	Mosquitoes (count)	2,448	13,501	4,376	48,928		297,98	38,599	61,714	53,346	29,399		
Traps (count)	28	28	28	27	111	28	27	28	28	28	111	
Mosquito/night/trap	87.4	482.2	156.3	1,812.1		1,064.2	1,429.6	2,204.1	1,905.2	1,050.0		
Significant differences indicate in bold font.

Socioeconomic and human demographic variables

Because sociodemographic factors have been linked to mosquito-borne disease risk, sociodemographic data were downloaded from the U.S. Census Bureau American Community Survey (ACS) 5-year estimates (2013 to 2018). This period was used as a better representation of the baseline (2016, 2018) and for comparison with the mosquito data (2017). The variables of interest are similar to those used in previous similar studies in Chicago, Detroit, Georgia, and New Orleans11,58–60 and include population density, percentage population Hispanic, African American, gender, median household income, education attainment (a bachelor’s degree or higher), and unemployment.

Meteorological variables

We obtained historical daily mean temperature and rainfall data reported at the Miami International Airport (MIA) Station for an equivalent study period (August 15, 2016, to October 15, 2018). The data included weekly maximum temperature, weekly minimum temperature, and weekly rainfall. We also obtained rainfall raster files for September 10 to 12, 2017, to represent the Hurricane Irma period from the National Weather Services Advanced Hydrologic Prediction Service in GeoTIFF format.

Administrative boundaries

Administrative and evacuation zone boundaries at the census tract level for 2010 were obtained from the Miami-Dade County GIS Hub (https://gis-mdc.opendata.arcgis.com/). The evacuation zone is divided into five risk levels, A to E, with A being the most vulnerable.

Data analysis

Data processing

Only female mosquito counts from the two study trap types were used in the analysis since only female mosquitos need blood meal for egg development. Given that the mosquito capture data was highly skewed, we used log transformation to reduce the skewness and stabilize the variance. Summary statistics were compiled by trap night for each study epi-week to denote the pre-post Hurricane Irma periods, with results presented for both epi-week variation and variation independent of year. We then geocoded all mosquito data from the identified active traps captured in both CDC and BG-Sentinel traps (n = 161 traps; 30 CDC light traps and 131 BG Sentinel traps) using ArcGIS version 10.361. Of the 161 mosquito traps maintained by the Miami-Dade County Mosquito Control and Habitat Management Division, 96 BG-Sentinel traps and 28 CDC light traps were active during the study period. The geocoded mosquito data were then assigned to their appropriate census tracts along with demographic and socioeconomic variables. To facilitate comparison over time, an average was used when multiple traps existed in a census tract. Of the 519 census tracts in Miami-Dade County, 13.7 (n = 71) were active before and after Hurricane Irma.

To extract the rainfall raster data points corresponding to the mosquito traps, we applied a focal statistics tool in ArcGIS version 10.3161 to derive mean rainfall values for each census tract. In ArcGIS, the ‘focal statistics’ tool calculates statistics for a focal cell within a defined moving window. For each input cell location, this tool computes a statistic based on the values within a specified neighborhood around it. To achieve this, it uses the maximum, minimum, and mean values of a cell from its neighboring cells. Users can directly calculate these values by specifying the required parameters in the ‘focal statistics’ window. In the context of this study, we calculated an input cell location and recorded a statistic of values within a specific census tract. This information was then spatially joined to the study points (mosquito traps) and appropriate census tracts for statistical analysis.

Statistical analysis

Having confirmed that the assumptions for analysis of variance (ANOVA)—normality, homogeneity of variances, and independence—were met during exploratory data analysis, we proceeded to perform an ANOVA. Our goal was to evaluate whether mosquito abundance in CDC light traps and BG-Sentinel traps differed before and after Hurricane Irma. The dependent variable was the number of adult female mosquitoes per trap at night by trap type. Independent variables of interest were the different mosquito species (categorical variable) and a binary dummy variable that depicted Hurricane Irma (coded as 0 = pre- and 1 = post-Hurricane Irma). Included in the analysis were 20 species captured in light traps and 13 species captured in BG Sentinel traps. These species are of public health importance by the Miami-Dade County Mosquito Control and Habitat Management Division. Analysis was performed using IBM SPSS Statistics for Windows Version 25.062.

Ordinary least squares (OLS) and geographically weighted regression (GWR)

We applied both ordinary least squares (OLS) and geographically weighted regression (GWR)63 models to assess the effects of sociodemographic and meteorological factors (temperature, rainfall, and sociodemographic data) pre- and post-Hurricane Irma and across census tracts in Miami-Dade County on mosquito abundance (average number of mosquitoes in each census tract per week). The detailed information and spatial distributions of the dependent and independent variables used for OLS and GWR are shown in Table 1. OLS was applied to evaluate the global relationships between the dependent and independent variables. Given the spatial heterogeneity of our study area Miami-Dade County and because OLS regression assumes that the data are normally distributed and that the regression coefficients are “global” and apply equally across the entire study area and that residuals are spatially random, this can bias the OLS scores and inflate their significance64. Specifically, we observed that residuals for the OLS regression model (via Moran’s I, a diagnostic statistic)65–67 were randomly distributed for mosquitoes captured pre–Hurricane Irma but were dispersed for those captured post-Hurricane Irma, which violates the assumption of OLS regression68.

Given these findings, we used GWR to help identify the spatial influence of census tracts/traps, which are not explained by OLS regression. We used cross-validation (CV) as the bandwidth method and opted for the adaptive kernel type in the analysis settings for GWR because the distribution of traps/census tracts was heterogeneous in the Miami-County study area. To compare the model performances of OLS and GWR, we used the coefficient of determination (R2) and corrected Akaike’s information criterion (AICc)63,69. To evaluate the extent of multicollinearity among the independent variables (meteorological correlates, temperature, rainfall, and sociodemographic factors) before and after Hurricane Irma, we used the variance inflation factor (VIF) for the set of all nine covariates prior to model fitting, where VIF values greater than 10 indicates problematic collinearity/ redundancy among the variables70. Multicollinearity was not indicated (values were less than 10) in full models, and all variables were used in the model71. The GWR and OLS analyses were conducted using GWR software (version 3.0; Newcastle University, England, UK).

Results

Mosquito abundance and composition by week and year

Between 2016 and 2018, a total of 535,095 adult female mosquitoes comprising 32 mosquito species were captured on 1,248 trap nights in the two trap types. Of the trap nights, 609 trap nights were conducted before Hurricane Irma, and 639 trap nights were conducted after Hurricane Irma (Table 4). The mosquito abundance was 5.3 times greater in 2017 (406,842) than in 2016 (77,237) and 8.0 times greater in 2017 (406,842) than in 2018 (51,017). Cx. nigripalpus represented 70.4% of the 535,096 mosquito captures, followed by Aedes taeniorhynchus (10.1%), Aedes albopictus (7.8%), Aedes tortillis (6.4%), Aedes aegypti (3.6%), and Cx. quinquefasciatus (3.5%). From the data in Table 4, and for some species, the number of mosquitoes collected in light traps varied significantly over the study years. For example, the mean numbers of Cx. nigripalpus per trap during 2016 and 2017 were 156.9 and 1,259.2, respectively. Figure 2 shows areas in Miami-Dade County for example, that experienced high numbers of Cx. nigripalpus during epi-week 36–38.Table 4 Total number of adult female mosquitoes collected in BG-Sentinel traps and CDC- light traps in Miami-Dade County over 8 weeks (epi-week 33–36 and epi week 38–41) in 2016–2018.

	CDC light traps	BG-Sentinel traps	CDC light traps
(per trap night)	BG-Sentinel traps
(per trap night)	
Year/trap Night	2016
(n = 176)	2017
(n = 235)	2018
(n = 240)	2016
(n = 523)	2017
(n = 1013)	2018
(n = 1,182)	2016	2017	2018	2016	2017	2018	
Species													
Aedes aegypti	236	501	388	20,233	6,982	7,145	1.34	2.13	1.62	38.69	6.89	6.04	
Aedes albopictus	275	199	276	28	38	115	1.56	0.85	1.15	0.05	0.04	0.10	
Aedes atlanticus	7887	1675	1151	27	5	4	44.81	7.13	4.80	0.05	0.00	0.00	
Aedes bahamensis	0	2	19	0	0	40	0.00	0.01	0.08	0.00	0.00	0.03	
Aedes fulvus	0	0	0	0	0	1	0.00	0.00	0.00	0.00	0.00	0.00	
Aedes infirmatus	0	250	11	0	1	0	0.00	1.06	0.05	0.00	0.00	0.00	
Aedes mitchellae	0	0	0	0	0	1	0.00	0.00	0.00	0.00	0.00	0.00	
Aedes scapularis	0	1	0	0	0	0	0.00	0.00	0.00	0.00	0.00	0.00	
Aedes taeniorhynchus	4,985	39,936	905	446	3,052	39	28.32	169.94	3.77	0.85	3.01	0.03	
Aedes tortilis	4,429	19,976	1,235	163	3,126	305	25.16	85.00	5.15	0.31	3.09	0.26	
Aedes triseriatus	28	141	65	52	91	62	0.16	0.60	0.27	0.10	0.09	0.05	
Anopheles albimanus	0	84	0	0	0	0	0.00	0.36	0.00	0.00	0.00	0.00	
Anopheles atropos	177	683	79	7	6	5	1.01	2.91	0.33	0.01	0.01	0.00	
Anopheles crucians	1,716	7,007	1,144	51	167	112	9.75	29.82	4.77	0.10	0.16	0.09	
Anopheles punctipennis	0	8	0	0	0	73	0.00	0.03	0.00	0.00	0.00	0.06	
Anopheles quadrimaculatus	25	692	257	7	20	0	0.14	2.94	1.07	0.01	0.02	0.00	
Anopheles walkeri	0	0	0	0	0	0	0.00	0.00	0.00	0.00	0.00	0.00	
Coquillettidia perturbans	13	2	3	1	0	0	0.07	0.01	0.01	0.00	0.00	0.00	
Culex atratus	96	341	0	9	6	1	0.55	1.45	0.00	0.02	0.01	0.00	
Culex biscaynensis	0	0	0	0	1	5	0.00	0.00	0.00	0.00	0.00	0.00	
Culex cedecei	0	0	0	0	0	0	0.00	0.00	0.00	0.00	0.00	0.00	
Culex coronator	179	651	536	63	350	310	1.02	2.77	2.23	0.12	0.35	0.26	
Culex erraticus	387	3,420	3772	47	219	324	2.20	14.55	15.72	0.09	0.22	0.27	
Culex interrogator	0	0	115	0	0	2	0.00	0.00	0.48	0.00	0.00	0.00	
Culex iolambdis	0	0	0	0	0	0	0.00	0.00	0.00	0.00	0.00	0.00	
Culex nigripalpus	27,621	295,914	20,441	1,200	3,857	980	156.94	1,259.21	85.17	2.29	3.81	0.83	
Culex quinquefasciatus	75	1,922	281	4,556	7,394	5,076	0.43	8.18	1.17	8.71	7.30	4.29	
Deinocerities cancer	395	1,233	1,503	15	82	6	2.24	5.25	6.26	0.03	0.08	0.01	
Mansonia dyari	10	96	224	4	37	5	0.06	0.41	0.93	0.01	0.04	0.00	
Mansonia titillans	8	51	61	23	0	2	0.05	0.22	0.25	0.04	0.00	0.00	
Psorophora johnstonii	0	0	0	0	0	0	0.00	0.00	0.00	0.00	0.00	0.00	
Psorophora ciliata	0	48	0	0	2	0	0.00	0.20	0.00	0.00	0.00	0.00	
Psorophora columbiae	59	1,822	125	8	755	38	0.34	7.75	0.52	0.02	0.75	0.03	
Psorophora ferox	694	2,971	26	1	2	0	3.94	12.64	0.11	0.00	0.00	0.00	
Psorophora pygmea	0	113	0	0	7	0	0.00	0.48	0.00	0.00	0.01	0.00	
Uranotaenia lowii	0	32	0	0	1	0	0.00	0.14	0.00	0.00	0.00	0.00	
Uranotaenia sapphirina	0	33	1	1	0	0	0.00	0.14	0.00	0.00	0.00	0.00	
Wyeomya mitchelli	118	121	71	722	120	2,437	0.67	0.51	0.30	1.38	0.12	2.06	
Wyeomya vanduzeei	41	232	102	119	364	1,138	0.23	0.99	0.43	0.23	0.36	0.96	
Yearly totals	49,454	380,157	32,791	27,783	26,685	18,226							
Yearly totals (both trap types)	77,237	406,842	51,017										
Significant differences indicate in bold font.

Fig. 2 Maps of the weekly mean Cx. nigripalpus abundance in light and gravid traps from 2016 to 2018 in Miami-Dade County, Florida. 61

Source: Miami-Dade Mosquito Control and Habitat Management Division, MDC, Florida, 2018. Maps generated by the first author using ArcGIS software version 10.31

Mosquito abundance pre- and post-Hurricane Irma

Figure 3 indicates that the number of mosquitoes increased immediately (one week) after Hurricane Irma at week 38 and decreased slightly beginning at weeks 39 and 40 before increasing again one week later (i.e., week 41) (Fig. 3). As indicated at 33, 34 and 35 epi-weeks in 2016, no data from the BG-Sentinel traps were reported, as these traps were established only in September 2016 (i.e., week 46). The map shows that this species was abundant post-Hurricane Irma in traps and census tracks located in the Homestead, Pembroke Pines, and Kendall neighborhoods. Most of these areas were also refuge (evacuation zones).Fig. 3 Estimates of mean weekly mosquito captures per trap night, 2016 to 2018, Miami-Dade County, Florida.

Source: Miami-Dade Mosquito Control and Habitat Management Division, MDC, Florida, 2018.

Differences in mosquito abundance according to trap type pre- and post-Hurricane Irma

Table 5 presents the summary statistics for the two-way factorial ANOVA. The binary dummy variable for Hurricane Irma was a predictor of mosquito species abundance [Factorial ANOVA: F (25, 78) = 11.436, p < 0.001, R2 = 0.786] across all study census tracts in Miami-Dade County. Further, Table 5 shows that the effect of Hurricane Irma on mosquito abundance and species varied by trap type and between the two study periods (pre- and post-Hurricane Irma), suggesting that flood events can change the physical environment to favor an increase in the breeding of some mosquito species [F (1, 120) = 36.833, p < 0.001] especially for CDC light traps [F (39, 120) = 12.89; p < 0.001, R2 = 0.807] compared to pre-hurricane Irma and for mosquito species [F (19, 120) = 20.678, p < 0.001].Table 5 Results of two-way factorial ANOVA on the effect of the hurricane variable on mosquito abundance by trap type, 2016–2018, Miami-Dade County, Florida.

	Type III Sum of Squares	df	Mean Square	F	P Value	Partial η2	
BG-sentinel traps	
 Model	10.681a	25	.427	11.436	.000	.786	
 Hurricane Irma variable	.560	1	.560	14.979	.000	.161	
 Species	8.864	12	.739	19.771	.000	.753	
 Species *Hurricane Irma Variable	1.258	12	.105	2.805	.003	.301	
 Error	2.914	78	.037				
 Total	13.596	103					
a. R Squared = .786 (Adjusted R Squared = .717)	
CDC Light Traps	
 Model	75.092a	39	1.925	12.890	.000	.807	
 Species	58.686	19	3.089	20.678	.000	.766	
 Hurricane Irma variable	5.502	1	5.502	36.833	.000	.235	
 Species * Hurricane variable	10.904	19	.574	3.842	.000	.378	
 Error	17.925	120	.149				
 Total	93.017	159					
a. R Squared = .807 (Adjusted R Squared = .745)	

Of the 13 species of mosquitoes captured in the BG-Sentinel traps before and after Hurricane Irma, Ae. tortills (p < 0.05, 95% CI [− 0.683, − 0.139]), Cx. nigripalpus (p < 0.05, 95% CI [− 0.968, − 0.424]) and Cx. quinquefasciatus (p < 0.05, 95% CI [-0.669, -0.124]) were the most common. For CDC light traps, seven species dominated pre- and post-Hurricane Irma: Aedes tortills (p < 0.05, 95% CI [− 1.466, − 0.384]); An. atroparvus (p < 0.05, 95% CI [− 1.117, − 0.035]); Anopheles crucians (p < 0.05, 95% CI [-1.591, -0.509]; An. quadrimaculatus (p < 0.05, 95% CI [− 144, − 0.062]); Cx. erraticus (p < 0.05, 95% CI [-1.316, − 0.234]; Cx. nigripalpus (p < 0.05, 95% CI [2.400, − 1.317]; and Ps. columbiae (p < 0.05, 95% CI [− 1.334, − 0.251]) (Table 6).Table 6 Univariate test summary results by species and trap type, 2016–2018, Miami-Dade County, Florida.

Mosquito Species	CDC light traps	BG-Sentinel traps	
	F	η2	95% CI	P value	F	η2	95% CI	P value	
Aedes aegypti	.271	.002	− .399–.683	.603	.010	.000	− .286–.258	.920	
Aedes albopictus	.357	.003	− .378–.704	.551	.316	.003	− .387–.695	.575	
Aedes taeniorhynchus	.612	.005	− .327–0.755	.436	.052	.001	− .241–.303	.821	
Aedes tortilis	11.464	.087	− 1.466–-.384	.001	9.050	.104	− .683–− .139	.004	
Aedes triseriatus	.332	.003	− .384–.699	.565	.038	.000	− .246–.299	.847	
Anopheles albimanus	.496	.004	− .734–.349	.482	–	–	–	–	
Anopheles atropos	4.443	.036	− 1.117–− .035	.037	.000	.000	− .270–.274	.989	
Anopheles crucians	14.762	.110	− 1.591–− .509	.000	.053	.001	− .304–.241	.819	
Anopheles quadrimaculatus	4.871	.039	− 1.144–-.062	.029	–	–	–	–	
Culex atratus	1.418	.012	− .866–.216	.236	–	–	–	–	
Culex coronator	3.447	.028	− 1.048—.034	.066	.995	.013	− .408–.136	.322	
Culex erraticus	8.035	.063	− 1.316–− .234	.005	.214	.003	− .335–.209	.645	
Culex nigripalpus	46.242	.278	− 2.400–− 1.317	.000	25.905	.249	− .968–− .424	.000	
Culex quinquefasciatus	.198	.002	− .663–.419	.657	8.412	.097	− .669–− .124	.005	
Deinocerities cancer	2.563	.021	− .979–.104	.112	–	–	–	–	
Mansonia dyari	.271	.002	− .683–.399	.603	–	–	–	–	
Psorophora columbiae	8.411	.065	− 1.334–− 251	.004	3.607	.044	− .532–.013	.061	
Psorophora ferox	.487	.004	− .732–.350	.487	–	–	–	–	
Wyeomya mitchelli	–	–	–	–	.045	.001	− .301–.243	.833	
Wyeomya vanduzeei	.834	.007	− .291–.791	.363	.259	.003	− .203–.342	.612	
CI Confidence interval. Each F tests the simple effects of hurricane within each level combination of the other effects shown. These tests are based on linearly independent pairwise comparisons among the estimated marginal means to test the effects of Hurricane Irma on mosquito abundance by species in BG-Sentinel traps and CDC light traps. – This species was not captured in this trap type.

Significant differences indicate in bold font.

Risk factors for mosquito abundance

The seven risk factors of interest were analyzed using OLS and GWR (Table 7). The global regression (OLS) model demonstrated that meteorological factors (rainfall and temperature during the week of mosquito collection), population density, and the percentage of the female population were negatively associated with mosquito abundance in Miami-Dade County before and after Hurricane Irma; however, the other correlations were not significant.Table 7 Summary of the ordinary least squares (OLS) regression model, 2016–2018, Miami-Dade County, Florida.

Variable	Coefficient	Std Error	t-Statistic Probability	VIF	
Population density	− 0.015992	0.007661	− 2.087334	0.040908	1.106067	
Education	− 0.011455	0.271040	− 0.042262	0.966421	1.114925	
% Population unemployment	4.659061	3.000470	1.552777	0.125490	1.507621	
% Population Hispanic	− 81.175786	103.337491	− 0.785541	0.435078	2.038505	
% Population black	− 141.880734	117.449151	− 1.208018	0.231555	3.086766	
% Population female	− 710.633649	219.375682	− 3.239346	0.001917	1.532289	
Median household income	− 0.000043	0.000609	-0.069987	0.944423	1.914037	
Rainfall	− 0.015992	0.007661	− 2.087334	0.030908	1.107867	
Temperature	− 0.011455	0.271040	− 0.042262	0.0021 56	1.115025	
Significant differences indicate in bold font.

The GWR model showed the detailed spatial distribution of the associations between mosquito abundance and risk factors in Miami-Dade County. The correlations showed high variability in terms of geography. Unlike in the OLS model, all the independent variables were not significant (data not shown), suggesting spatial non-stationarity in the relationships between mosquito abundance and the independent variables of interest.

Discussion

This study attempted to examine adult mosquito abundance and species composition and estimate the time lags required for the development and recovery of potential vector populations before and after Hurricane Irma in Miami-Dade County, Florida. To our knowledge, this is the first study to assess the pre-post effects of hurricanes on mosquito abundance and species composition at the census tract level in Miami-Dade County. Prior studies conducted in the continental U.S. have linked the effects of storm events on mosquito abundance, but the results have been mixed45. Until recently, (since Hurricane Katrina), very few studies have monitored mosquito populations and species composition before and after storm events in the continental U.S.11,27,34,39–41,72. In particular, the effects of post-disaster time lags have rarely been investigated. In the context of this study, we observed a surge in mosquito populations after Hurricane Irma in Miami-Dade County. While not every mosquito is a vector, mosquito-borne arboviruses such as WNV, SLE, and EEE as well as increases in the mosquito population have been reported post storm in Florida8,46–48. For this reason, Florida including Miami-Dade County with their past transmission are at risk for arbovirus transmission post-storm38,46–48. Moreover, Miami-Dade County was the epicenter of the 2016 Zika outbreak in continental U.S.12,13,73–75.

To mitigate and prevent future risks, we must enhance mosquito surveillance, raise public awareness, and implement targeted vector control measures. We also found that more mosquitoes (7.3 and 8.0 times more) were captured in 2017 than at baseline (2016 and 2018). A possible explanation for this difference might be that more trap nights were carried out post-Hurricane Irma (639 trap nights) than at baseline (609 trap nights), respectively (see Table 3). We also observed an immediate increase in the mean number of mosquitoes immediately (one week) after Hurricane Irma (epi-week 38). This was followed by a slight decrease two weeks later during epi-weeks 39 and 40 before increasing again in epi-week 41 (three weeks post-Hurricane Irma). There is typically a one-week lag before mosquito activity increases due to factors like standing water accumulation and altered breeding habitats. Some research suggests this lag might extend up to four weeks, possibly due to delayed impacts on mosquito life cycles81,82. Regarding recovery, species resilience varies, with some mosquitoes rebounding faster than others. Notably, 32 species were collected in this study period and there are a diverse range of responses to effects of the hurricane therefore this is an important issue for future research. Notably, 32 species were collected during this study period, exhibiting a diverse range of responses to the effects of the hurricane. This variability underscores the importance of further research on this issue. Moise and colleagues’ study on environmental and socio-demographic predictors of the southern house mosquito, Culex quinquefasciatus, in New Orleans, Louisiana, found that highly developed areas were negatively associated with the abundance of Cx. quinquefasciatus11.

In our study, Cx. nigripalpus was by far the most common mosquito collected during the study (70.4% of all mosquito species collected). This finding is not surprising considering that this species of mosquito is the most important disease vector in Florida and is reproductively active throughout the year in South Florida83,84, particularly during the summer and early fall85. Cx. nigripalpus has been identified as a vector of SLEV86,87. In Florida, field data has also implicated this species as a predominant vector in SLEV epidemics88–92. Cx. nigripalpus activities are also known to increase when overall humidity increases with the onset of the rainy season84. This also accords with earlier observations, which showed that Cx. nigripalpus actively explores open areas and utilizes a range of aquatic habitats, varying in nutrient content and seasonal availability84,93. Another possible explanation for this and in the context of our study is that it’s possible that Hurricane Irma facilitated suitable conditions, particularly temperature, precipitation, and relative humidity94,95, including recently flooded habitats that allowed gravid Cx. nigripalpus females to emerge from underground culverts and gopher tortoise burrows where they overwintered, kickstarting the population surge typically seen immediately (the week) following Hurricane Irma. This finding supports evidence from previous observations84,93,96–100. Additionally, these findings suggest that after heavy rainfall, Cx. nigripalpus mosquitoes adapt by seeking saturated resting spots in densely vegetated areas, a finding consistent with that of Day and Curtis97 who noted Cx. nigripalpus’ daily movement as closely following rainfall patterns.

Although the responses of different mosquito species to the possible effects of Hurricane Irma varied, a note of caution is warranted. For example, mosquito surveillance conducted with CDC light traps and CDC gravid traps naturally yields different community compositions, which should be considered in future studies post Hurricanes. In our study and among the mosquito species captured in the BG-Sentinel traps, 3 of the 13 species (Ae. tortills, Cx. nigripalpus, and Cx. quinquefasciatus) were dominant post-Hurricane Irma, whereas among the mosquitoes captured using CDC light traps, 7 species were commonly collected (Ae. tortills, An. atropos, An. crucians, An. quadrimaculatus, Cx. erraticus, Cx. nigripalpus, and Ps. columbiae). Further research should also be carried out to investigate the underlying factors that contribute to this varied mosquito species response. This may further our understanding of the relationships between the amount of rainfall and other underlying factors affecting mosquito abundance.

A GWR revealed spatial non-stationarity in the relationships between mean mosquito abundance and risk factors such as human population density, rainfall, and temperature. Warmer temperatures are known to accelerate larval development, leading to the faster emergence of adult mosquitoes. However, extreme temperatures can be detrimental. Rainfall influences the availability of suitable larval habitats, with increased rain creating more breeding sites. Both temperature and rainfall are critical for mosquito survival, development, and disease transmission potential.

One of the key issues highlighted by these findings is the connection between human population density and mosquito abundance. This observation aligns with earlier research, emphasizing the intricate interplay between human communities and mosquito populations. Factors such as urbanization, population movement, and changing mobility patterns all play a role in shaping the dynamics of mosquito-borne disease transmission105,106. Overall, the results underscore the limitations of using a single set of global parameters to model the distribution of mosquito risk factors across Miami-Dade County and that temperature and rainfall during the week of mosquito collection significantly affect larval and adult survival11,101–104.

The present results are significant in at least two major respects. First, seven primary vectors of human disease predominated after Hurricane Irma in Miami-Dade County: Ae. tortilis, An. atropos, An. crucians, An. quadrimaculatus, Cx. erraticus, Cx. nigripalpus, and Ps. columbiae. Second, different environmental and sociodemographic factors influence both mosquito abundance and mosquito species in Miami-Dade County. This information can help guide those working in public health preparedness and disaster response. Additional research is needed to better understand the potential for mosquito-borne disease transmission.

Acknowledgements

A special thank you to Miami-Dade County Mosquito Control and Habitat Management Division field inspectors who monitor mosquito surveillance traps Dr. Ephantus J. Muturi of the Crop Bioprotection Research, USDA in Peoria for reviewing this manuscript.

Author contributions

IKM and QH conceived, analyzed, and designed the study. WP and JPM were responsible for the mosquito collection and taxonomic identification. IKM and QH developed the study methodology and data analysis methodologies. QH wrote the original draft of the paper. All the authors contributed to reviewing and editing the paper.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

No humans or animals were involved in this study.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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