Current and Projected Distributions of Aedes aegypti and Ae. albopictus in Canada and the U.S.
2020; National Institute of Environmental Health Sciences; Volume: 128; Issue: 5 Linguagem: Inglês
10.1289/ehp5899
ISSN1552-9924
AutoresSalah Uddin Khan, Nicholas H. Ogden, Aamir Fazil, Philippe Gachon, Guillaume Dueymes, Amy L. Greer, Victoria Ng,
Tópico(s)Insect symbiosis and bacterial influences
ResumoVol. 128, No. 5 ResearchOpen AccessCurrent and Projected Distributions of Aedes aegypti and Ae. albopictus in Canada and the U.S. Salah Uddin Khan, Nicholas H. Ogden, Aamir A. Fazil, Philippe H. Gachon, Guillaume U. Dueymes, Amy L. Greer, and Victoria Ng Salah Uddin Khan Address correspondence to Salah Uddin Khan, National Microbiology Laboratory, Public Health Agency of Canada, 370 Speedvale Ave. W., Guelph, ON N1H 7M7 Canada and The Department of Population Medicine, University of Guelph, 50 Stone Rd. E., Guelph, ON N1G 2W1 Canada. Email: E-mail Address: [email protected], or Victoria Ng, Email: E-mail Address: [email protected] Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Ontario, and Saint-Hyacinthe, Québec, Canada Search for more papers by this author , Nicholas H. Ogden National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Ontario, and Saint-Hyacinthe, Québec, Canada Search for more papers by this author , Aamir A. Fazil National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Ontario, and Saint-Hyacinthe, Québec, Canada Search for more papers by this author , Philippe H. Gachon Étude et Simulation du Climat à l’Échelle Régionale centre, Université du Québec à Montréal, Québec, Canada Search for more papers by this author , Guillaume U. Dueymes Étude et Simulation du Climat à l’Échelle Régionale centre, Université du Québec à Montréal, Québec, Canada Search for more papers by this author , Amy L. Greer Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada Search for more papers by this author , and Victoria Ng Address correspondence to Salah Uddin Khan, National Microbiology Laboratory, Public Health Agency of Canada, 370 Speedvale Ave. W., Guelph, ON N1H 7M7 Canada and The Department of Population Medicine, University of Guelph, 50 Stone Rd. E., Guelph, ON N1G 2W1 Canada. Email: E-mail Address: [email protected], or Victoria Ng, Email: E-mail Address: [email protected] National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Ontario, and Saint-Hyacinthe, Québec, Canada Search for more papers by this author Published:22 May 2020CID: 057007https://doi.org/10.1289/EHP5899Cited by:4AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Aedes aegypti and Ae. albopictus are mosquito vectors of more than 22 arboviruses that infect humans.Objectives:Our objective was to develop regional ecological niche models for Ae. aegypti and Ae. albopictus in the conterminous United States and Canada with current observed and simulated climate and land-use data using boosted regression trees (BRTs).Methods:We used BRTs to assess climatic suitability for Ae. albopictus and Ae. aegypti mosquitoes in Canada and the United States under current and future projected climates.Results:Models for both species were mostly influenced by minimum daily temperature and demonstrated high accuracy for predicting their geographic ranges under the current climate. The northward range expansion of suitable niches for both species was projected under future climate models. Much of the United States and parts of southern Canada are projected to be suitable for both species by 2100, with Ae. albopictus projected to expand its range north earlier this century and further north than Ae. aegypti.Discussion:Our projections suggest that the suitable ecological niche for Aedes will expand with climate change in Canada and the United States, thus increasing the risk of Aedes-transmitted arboviruses. Increased surveillance for these vectors and the pathogens they carry would be prudent. https://doi.org/10.1289/EHP5899IntroductionMosquito-borne diseases (MBDs) account for approximately 350 million cases of human illness each year (WHO et al. 2017). Approximately 5% of infectious diseases are attributed to diseases transmitted by two mosquito species: Aedes aegypti and Ae. albopictus. These species are vectors for more than 22 arboviruses of global public health importance, including dengue, chikungunya, Zika, Japanese encephalitis, Rift Valley fever, yellow fever, and West Nile viruses (Medlock et al. 2015; Schaffner et al. 2013). With climate change, rising temperature and changes in precipitation patterns (Blunden and Arndt 2019; IPCC 2018) are expected to permit changes to the geographic range of these species, including poleward range expansion in North America (Bonizzoni et al. 2013). Concurrently, it is clear that North American travelers to tropical and subtropical regions can acquire arbovirus infections, with a proportion viremic when they return home and acting as a source of infection for Aedes mosquitoes that may be present (Drebot et al. 2015; Khan et al. 2014; Ogden et al. 2017; Petersen et al. 2016). As a consequence, autochthonous transmission of MBDs previously considered endemic to tropical and subtropical regions may become more frequent in current temperate regions (Ng et al. 2017; Ogden 2017).During recent years, there have been multiple reports of autochthonous transmission of MBDs leading to localized epidemics of Zika virus, chikungunya virus, and dengue virus infections in humans in the southern continental United States due to the establishment of local Aedes mosquito populations and range expansion (Hahn et al. 2016; Kendrick et al. 2014; Likos et al. 2016; Ramos et al. 2008). Canada’s mosquito surveillance programs are primarily targeted toward mosquitoes carrying endemic pathogens of public health concerns such as West Nile virus and eastern equine encephalitis. However, these surveillance programs are capable of detecting mosquito species exotic to Canada (i.e., Aedes spp.), which results in targeted active surveillance in specific regions. During 2016–2017, Ae. aegypti was found in low abundance during the summer months in southwestern Ontario in Canada; the most northern known occurrence in continental North America in recent years (WECHU 2017). This observation triggered additional active surveillance for Aedes mosquitoes in this region. However, mosquito trapping from 2018 did not identify Ae. aegypti in that region (WECHU 2017). Since its introduction into continental North America in 1985 (Sprenger and Wuithiranyagool 1986), Ae. albopictus is now frequently reported from the U.S. southern to upper Midwestern states, some northeastern states, and southern regions of the northwestern states of the United States and along the Pacific coast (Hahn et al. 2016, 2017; Kraemer et al. 2015a), increasingly pushing through their hypothesized northern boundaries (Nawrocki and Hawley 1987). In Canada, Ae. albopictus was consistently reported in southwestern Ontario from 2016 to 2018 (Nelder and Russell 2019; Awuor et al. 2019, WECHU 2018). Ae. albopictus is currently considered to be locally established in this region (M. Nelder and C. Russell, personal communication).Changes in temperature are expected to be a major driver of changes in geographic ranges because temperature affects the fundamental biological processes of the mosquitoes, including survival and interstadial development rates (and thus life span) and reproduction rates, which determine where and when populations can persist in particular locations (Couret et al. 2014; Dell et al. 2011; Mordecai et al. 2017). The rate of average global temperature change is now increasing more rapidly (IPCC 2018), and since the year 2000 an accelerated warming has been observed globally (Blunden and Arndt 2019). Canada has warmed twice as fast as the rest of the world, and the Canadian North has warmed three times as fast, over the last seven decades (i.e., 1948–2017) (Zhang et al. 2019).In recent years, several global and regional models have been developed to describe the current ecological niche and possible geographic distribution of Ae. aegypti and Ae. albopictus mosquitoes (Campbell et al. 2015; Ding et al. 2018; Johnson et al. 2017; Kraemer et al. 2015a; Nawrocki and Hawley 1987). The approaches used ranges from defining thermal limits (Nawrocki and Hawley 1987) and temperature suitability indices (Brady et al. 2014) of Ae. aegypti and Ae. albopictus distributions to statistical and machine learning approaches in order to develop global (Campbell et al. 2015; Ding et al. 2018; Kraemer et al. 2015a) and regional (Johnson et al. 2017) ecological niche models of these species. These are useful guides; however, in many instances, a wide range of time periods (e.g., 1960s to 2016) were considered for Aedes mosquitoes occurrence data to be incorporated to the models (Ding et al. 2018; Johnson et al. 2017; Kraemer et al. 2015a). To our knowledge, model-based assessments of future distributions of these Aedes spp. mosquitoes have only been attempted at the global scale (Kraemer et al. 2019; Ryan et al. 2019), at a regional scale for Ae. albopictus up to 2070 (Ogden et al. 2014), and at a local scale in the northeastern United States (Rochlin et al. 2013). In almost all cases, the recent shifts in the climate that could influence the recent changes in the distribution of mosquitoes and regional climatic variability were not addressed. Our objective was to develop regional ecological niche models for Ae. aegypti and Ae. albopictus in the conterminous United States and Canada with current observed and simulated climate and land-use data using boosted regression trees (BRTs). We then used output from an ensemble simulation of regional climate models (RCMs) to project possible changes to the geographic range of Ae. aegypti and Ae. albopictus, and to the human population at risk of Aedes-borne infections, in Canada and the United States from 2011 to 2100.MethodsEcological Niche Modeling ApproachTo model the ecological niche of the Aedes mosquitoes, we utilized the BRT model, which is a powerful tool for modeling complex nonlinear dependencies, identifying interactions between predictors, and avoiding over-fitting (Elith et al. 2008). We employed the following steps of data manipulation and analyses to predict the ecological niche suitability: a) We compiled a list of the two Aedes species occurrence data from multiple databases from Canada and the United States from 2001 to 2016. b) We compiled climatic and urban land cover data from 2001 to 2016. c) We developed an ecological niche model using BRTs for the current time period (2001–2016) to obtain climatic and urban land cover predictors of the occurrence of the Aedes species to identify the geographic limits of their ecological niches under the current climate. d) We used projected climatic data under moderate (RCP4.5) and high (RCP8.5) emission scenarios from four RCM simulations to project ecological niches for the Aedes species from 2011 to 2100. Finally, e) we developed ensemble ecological niche models from the individual RCM under future climatic conditions.Aedes Mosquito Vector Occurrence DataWe reviewed the existing Ae. aegypti and Ae. albopictus occurrence databases in Canada and the United States between 2001 and 2016 and identified multiple sources that were credible and comprehensive. These included the data presented in reports by Kraemer et al. (2015b) and Hahn et al. (2016, 2017) in addition to data from the Centers for Disease Control and Prevention ArboNet surveillance system database ( https://wwwn.cdc.gov/arbonet/). Data were also obtained from records held in the following institutions: Mevlabs, Inc., U.S. Army Public Health Command Region–North, and Walter Reed Biosystematics Unit (WRAIR, Division of Entomology) accessed through the VectorMap data portal ( http://vectormap.si.edu/dataportal.htm) and also from Windsor-Essex County Health unit vector surveillance (WECHU 2017, 2018). We merged the Aedes mosquito occurrence records from multiple sources after de-duplicating records, cross-checking, and georeferencing in ArcGIS™ (version 10.4.1, (Esri®) (see Excel Tables 1.1 and 1.2). To reduce redundancy of the number of reports across the databases, we included only a single reported geographical information system coordinate of mosquito occurrence in a location per year.Current Climatic and Urban Land Cover DataWe selected the climatic and urban land cover information in the model based on their known influence on the survival, life span, and reproductive rates of Aedes species mosquitoes. The temperature and vegetation index data was acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform, which captures high-resolution land surface temperature data on a daily basis; average precipitation data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) (Gelaro et al. 2017), which benefits from the integration of recent forecast model updates; and urban land cover data from the Global Rural-Urban Mapping project (GRUMP), version 1.01, which utilized observations of lights at night to assess urbanicity (see Table S1). Average, minimum, and maximum temperature, including the temperature in the coldest month (January), are known to influence survival and reproduction in both species (Brady et al. 2014; Mordecai et al. 2017). An average daily temperature threshold at 10°C has been linked to breeding and survival in Ae. albopictus (Delatte et al. 2009; Kobayashi et al. 2002), whereas an average daily temperature threshold at 20°C has been linked to larval-to-adult survival in Ae. aegypti under experimental conditions (Rueda et al. 1990). Precipitation is a likely possible determinant of the presence of suitable larval habitats for both species, as this may increase water in containers that hold rainwater, which is an breeding habitat for both species (Morrison et al. 2004). Vegetation in combination with precipitation can serve as a proxy for the availability of breeding habitat (Estallo et al. 2008) and for the survival of adult Aedes mosquitoes (Messina et al. 2016; Sota and Mogi 1992). Urbanization, as captured by urban land cover, acts as a proxy for the density of humans, who are one of the primary hosts for Ae. aegypti (Bargielowski et al. 2013). In contrast, Ae. albopictus has a broader host range and is also commonly found in rural and peri-urban areas (Ponlawat and Harrington 2005). Nevertheless, both mosquito vectors have been shown to lay eggs in artificial breeding grounds in urban areas (e.g., containers with stagnant water) (Li et al. 2014).Projected Climate and Urbanization Land Cover DataTo project the future ecological niche of the Aedes mosquitoes, we included mean, minimum, and maximum daily temperature; mean daily temperature in the coldest month of the year (January); number of days ≥10°C; number of days ≥20°C; and average total monthly precipitation from the data simulated by different RCMs under the two Representative Concentration Pathways (RCPs); (van Vuuren et al. 2011) and using model-derived urban land cover expansion data (Angel et al. 2011). The temperature and precipitation data were extracted from four RCMs with simulations using a 0.44° grid mesh (around a 50-km grid resolution): CanRCM4-CanESM2, CRCM5-CanESM2, CRCM5-MPI-ESM-LR, and HIRHAM5-EC-EARTH under moderate (RCP4.5) and high (RCP8.5) RCPs from series of simulated data sets of the North America Coordinated Regional climate Downscaling Experiment (CORDEX) project (Mearns et al. 2017) (see Table S2). These models project climate from 2006 to 2100. For each RCM, data from the 2006–2016 period were used to develop baseline BRT models, and which is hereafter referred to as the 2010 time point. Decision trees from the RCM-specific baseline BRT models were then used to predict Aedes species ecological niches for 30-y time (climatological) periods from 2011 to 2100. There were three time points used to illustrate projected changes: a) 2020, as a climatology computed between 2011 and 2040; b) 2050, as a climatology computed between 2041 and 2070; and c) 2080, as a climatology computed between 2071 and 2100.We calculated estimated urban land cover expansion from 2000 to 2050 using data presented by Angel et al. (2011). We fitted a linear regression over the North American urban land cover data from 2000 to 2050 due to the linear rate of the predicted urban land cover expansion and extrapolated the rate of urban land cover expansion until 2100. We applied this rate of urban expansion to the currently available global urban regional land cover data set from GRUMPv1 (CIESIN et al. 2017) and developed 30-y average urban land cover data for Canada and the United States from 2011 to 2100.Boosted Regression TreesWe performed an ensemble BRT modeling procedure similar to that reported by Bhatt et al. (2013) and Gilbert et al. (2014) in order to understand the climatic and urban land-use factors influencing the ecological niche of Ae. aegypti and Ae. albopictus mosquitoes and to project their distribution. This modeling approach is particularly useful in assessing complex nonlinear dependencies, identifying interactions between predictors, and avoiding over-fitting. We developed a bootstrapping algorithm that involved the following series of steps: We created pseudo controls to match Aedes mosquitoes’ occurrence by randomly generating one pseudo control for each occurrence based on the second-order spatial variation of the known distribution of Aedes mosquitoes (Berman and Diggle 1989).We developed a master data set by extracting the predictor values intersecting the mosquitoes’ presence and pseudo absence occurrence locations at a 1-km resolution in ArcGIS™ (version 10.4.1; (Esri®).We randomly sampled 80% of the data points from the master data set as a training data set for model building with a 10-fold cross validation and utilized the remaining data points for independent evaluation of the model (evaluation data set).We developed a BRT model using the training data set by using a stepwise procedure to jointly optimize the number of trees in a model, rate of learning, and the tree complexity. We also included a bag function of 0.5 to facilitate stochasticity in the models (Elith et al. 2008). At this stage, we also performed model simplification to identify the minimum set of predictors required for model building (Elith et al. 2008). Through this process, the vegetation index predictor from both models and the urban land cover predictor from the Ae. aegypti model were excluded as least contributing factors.We validated the model performance using the evaluation data set to assess the area under curve (AUC).Finally, we generated mosquito distribution maps by repeating Steps c to e over 120 iterations to generate niche distribution of Aedes mosquito vectors and partial dependency plots with means and 95% confidence intervals (CIs) of the relative influence (Friedman 2001) of the most influential predictors in the model.Once the probability of an ecological niche was defined, we utilized true skill statistics (sensitivity + specificity− 1) (Allouche et al. 2006) to identify a threshold cutoff value for the probability distribution to categorically define the presence or absence of ecological niche models. A probability value greater than or equal to the threshold cutoff defined the presence of suitable ecological niche in a location. When both species had a suitable ecological niche on a geographical location, a niche overlap was considered. These steps were utilized for BRT models both for the current and projected climatic scenarios, except the projected climatic model went through a single iteration in the BRT modeling Step f. Additional descriptions on BRT model fitting, simplification, and R codes are in the Supplemental Material in “Section S9.”The observed and projected climatic data were derived differently; one captured the observed climatic conditions and the latter was derived from RCM-coupled global climate model (CGCM) driven simulation models. This led us to develop a separate base model using simulated climatic conditions and urban land cover data for the current period and under future climatic conditions. We also considered the fact that the simulated climate data for both RCPs (4.5 and 8.5) close to the current timeline were similar because the model inputs (e.g., greenhouse gas concentration) were similar for this short time window close to the current period (van Vuuren et al. 2011). Therefore, we utilized climatic predictors from the four RCMs (RCP4.5 only) to develop BRT models for the time period 2006–2016 (base model), which was the only available projected data close to the observed climate data (2001–2016) used for BRT models to describe current ecological niche. Decision trees from the respective RCMs were used to simulate ecological niches for the two Aedes mosquitoes for the three climatological time windows (2020, 2050, and 2080). For each time period, we used the RCM-specific baseline BRT model’s AUC in the receiver operating characteristics (ROC) score (Breiner et al. 2015) to estimate a weighted ensemble model. The final estimate maps were generated at a resolution of 1-km using R (version 3.5.2; R Development Core Team).Population Living within the Predicted Aedes NicheWe utilized the projected global population grids from 2011 to 2100 (Jones and O’Neill 2016) to estimate the changes in the proportion of population in Canada and the United States living within the geographical regions’ suitable niche for Aedes mosquitoes. We took a conservative approach and utilized the projected population estimated through moderate Shared Socioeconomic Pathways (SSP2) scenarios, which account for demographic factors, urbanization, education, and other factors such as socioeconomic scenarios (Jones and O’Neill 2016). We calculated the proportion of the population living within the predicted Aedes niche corresponding to the time periods and RCPs. Finally, we performed a robust locally weighted nonparametric regression (Cleveland 1981) to estimate the changes in the proportion of the total population living within the projected ecological niche of Aedes mosquitoes.ResultsAedes Ecological Niches for the Current Time Period (2001–2016)We identified 341 unique occurrence data points for Ae. aegypti and 2,954 for Ae. albopictus from five databases. Both species were predominantly found in southern and southeastern regions of the United States. Although Ae. aegypti was sparsely distributed, the Ae. albopictus distribution was heavily concentrated within a region extending from the Central states to the East Coast (see Figure S1). One occurrence for Ae. aegypti and one for Ae. albopictus were in Canada (both in Windsor, ON, Canada) (see Figure S1 and Excel Tables S1 and S2 for a list of the Aedes species identified by occurrence year, state/province, and country).Based on BRTs, the probability of an ecological niche for Ae. aegypti at baseline (2001–2016) was highest in states in the southern and southeastern United States, with a northern boundary from southern New York to Kansas (Figure 1A). In addition, there was a relatively low probability of an ecological niche for Ae. aegypti along the West Coast of Canada and the United States (Figure 1A). The key predictors influencing the niche distribution of Ae. aegypti were average annual minimum daily temperature [relative contribution [RC=49.2% (95% CI: 48.2%, 50.1%)], annual maximum daily temperature [RC=13.1% (95% CI: 12.4%, 13.8%)], and mean daily temperature in January [RC=10.0% (95% CI: 9.7%, 10.2%)]; in combination, these predictors contributed to more than 70% of the regression tree decisions (Tables 1 and 2; see also Figure S2). All three predictors were positively associated with suitability for Ae. aegypti.Figure 1. Predicted ecological niche (probability from 0 to 1) for (A) Aedes aegypti and (B) Ae. albopictus mosquitoes, and (C) areas predicted to be an ecological niche for Aedes aegypti [True Skill Statistics (TSS): ≥0.69], Ae. albopictus (TSS: ≥0.80), in the continental United States and Canada under current climatic conditions (2001–2016). When both species had a suitable ecological niche in a geographical location, a niche overlap was considered.Table 1 Relative contribution (%) of the ecological factors contributing toward predicting the distribution of Aedes aegypti and Ae. albopictus mosquito vectors in Canada and the United States for the time period 2001–2016.Table 1 has three columns, namely, climatic and land use data, Aedes Aegypti mean (95 percent C I), and Aedes albopictus mean (95 percent C I).Climatic and land-use dataAe. aegypti [mean (95% CI)]Ae. albopictus [mean (95% CI)]Mean minimum daily temperature49.2 (48.2, 50.1)46.7 (45.8, 47.5)Mean maximum daily temperature13.1 (12.4, 13.8)—Number of days ≥10°C3.7 (3.6, 3.8)19.6 (18.7, 20.3)Mean daily temperature in January10.0 (9.7, 10.2)3.6 (3.6, 3.7)Number of days ≥20°C9.6 (9.4, 10.3)7.9 (7.6, 8.1)Mean total monthly precipitation6.0 (5.8, 6.2)8.7 (8.6, 8.8)Mean daily temperature8.1 (8.0, 8.4)5.4 (5.4, 5.5)Urban land cover—8.1 (8.1, 8.2)Note: The relative mean contribution and 95% confidence intervals (95% CI) for the covariates in the Aedes spp. models are presented in the respective columns. The covariates that contributed ≥10% to the model were considered most influential. —, indicates covariate dropout during model simplification process.Table 2 Relative contribution (%) of the ecological factors contributing toward predicting the distribution of Aedes aegypti and Ae. albopictus mosquito vectors in Canada and the United States for the time period 2006–2016, using projected climatic data from four regional climatic data models (RCP4.5) and a single boosted regression trees model run.Table 2 titled regional climatic models has six columns, namely, species, covariates, CanRCM4-CanESM2 (RC), CRCM5-CanESM2 (RC), CRCM5-MPI-ESM-LR (RC), and HIRHAM5-EC-EARTH (RC).SpeciesCovariatesRegional climatic modelsCanRCM4-CanESM2[RC (%)]aCRCM5-CanESM2[RC (%)]aCRCM5-MPI-ESM-LR[RC (%)]aHIRHAM5-EC-EARTH[RC (%)]aAedes aegyptiMean temperature8.2151.9220.1330.56Mean minimum temperature3.492.778.6136.84Mean maximum temperature6.4117.1615.454.48Mean January temperature43.4811.9013.030.00Number of days ≥10°C24.109.0119.4617.36Number of days ≥20°C2.561.6313.692.11Mean precipitation4.451.583.753.60Urban land cover7.344.035.875.04Aedes albopictusMean temperature5.883.9216.339.83Mean minimum temperature38.5441.5740.0211.04Mean maximum temperature————Mean January temperature3.414.145.330.00Number of days ≥10°C34.0033.7319.2352.03Number of days ≥20°C4.183.555.384.77Mean precipitation7.479.118.6714.36Urban land cover6.523.995.057.97Note: The covariates that contributed ≥10% to the model were considered most influential. —, indicates covariate dropout during model simplification process; RC, relative contribution.aThe RC values are rounded to two decimal digits.The niche for Ae. albopictus extended from southeastern regions of the United States to the south and southwestern borders of Ontario, Canada, and from the East Coast to the Central United States, and sporadically along the West Coast of both Canada and the United States (Figure 1). Primary factors associated with suitability for Ae. albopictus were annual minimum daily temperature [RC=46.7% (95% CI: 45.8%, 47.5%)] and annual average number of days ≥10°C [RC=19.6% (95% CI: 18.7%, 20.3%)]. In addition, the total mean monthly precipitation [RC=8.7% (95% CI: 8.6%, 8.8%)] and urban land cover [RC=8.1% (95% CI: 8.1%, 8.2%)]; all these factors collectively contributed to 83% of the regression tree decisions and were positively associated with suitability for Ae. albopictus (Table 1; see also Figure S3). BRT models validation statistics with external testing data over 120 iterations demonstrated good model fit for both species: Ae. aegypti [AUC=0.97 (95% CI: 0.96, 0.97)] and Ae. albopictus [AUC=0.95 (95% CI: 0.95, 0.95)].Figure 1A,B shows a probability distribution between of 0 to 1 for the ecological niche for the two species, and Figure 1C utilized True Skill Statistics to identify a threshold cutoff (see Table S3) for the probability values derived in the models presented in Figure 1A,B to explore ecological niche suitability with a presence–absence indicator and to identify the regions where there could be a niche overlap for both species. The niche overlaps for the mosquitoes were primarily in the southcentral to southeastern states of the United States and sporadically in the southern regions of the Illinois and New York state (Figure 1C). A sparsely distributed niche overlap was also predicted in the southern West Coast (Figure 1C).Aedes Ecological Niches for the Projected Time Period (2011–2100)The weighted ensemble models generated from four simulations of four different RCM-CGCM combinations—one RCM used two different boundary conditions from two CGCMs and two other RCMs with each used one CGCM as boundary conditions—had high AUC scores (0.96–0.97 for Ae. albopitus, 0.93–0.98 for Ae. aegypti) and consistency among models (see Table S4). For the simulated climate, the Ae. aegypti models were mostly influenced by mean and maximum daily temperature, temperatures in the coldest month of the year (January), number of days ≥10°C and ≥20°C, minimum daily temperature, and urban land cover. For Ae. albopictus, the models were primarily influenced by minimum daily temperature, number of days ≥10°C, daily mean temperature, total monthly precipitation, and urban land cover (Table 2; see also Figure S4). The ecological niche models using the observed and simulated climatic data for the current time periods (2001–2016 vs. 2006–2016) demonstrated similar distributions for both Aedes species (Figures 1 and 2–5, 2010 panels).Figure 2. Predicted probabilities for
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