Abstract
West Nile virus (WNV) is a mosquito-borne pathogen associated with uncommon but severe neurological complications in humans, especially among the elderly and immune-compromised. In Northeastern North America, the Culex pipiens/restuans complex and Aedes vexans are the two principal vector mosquito species/species groups of WNV. Using a 10-year surveillance dataset of WNV vector captures at 118 sites across an area of 40,000 km2 in Eastern Ontario, Canada, the ecological niches of Cx. pipiens/restuans and Aedes vexans were modeled by random forest analysis. Spatiotemporal clusters of WNV-positive mosquito pools were identified using Kulldorf’s spatial scan statistic. The study region encompasses land cover types and climate representative of highly populated Southeastern Canada. We found highest vector habitat suitability in the eastern half of the study area, where temperatures are generally warmer (variable importance > 0.40) and residential and agricultural cropland cover is more prominent (variable importance > 0.25). We found spatiotemporal clusters of high WNV infection rates around the city of Ottawa in both mosquito vector species. These results support the previous literature in the same region and elsewhere suggesting areas surrounding highly populated areas are also high-risk areas for vector-borne zoonoses such as the WNV.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
First discovered in Uganda in 1937 (Smithburn et al. 1940), West Nile virus (WNV) now has a wide global geographic range, including North America (Gubler 2007; Reisen 2013; Chancey et al. 2015). In North America, including Canada, a range of passerine bird species act as reservoir hosts, and the virus is transmitted among birds via several mosquito vector species (Kilpatrick et al. 2006; Marini et al. 2017). A large number of mammalian, reptilian and amphibian species, including humans, may be incidental hosts of the virus (Artsob et al. 2006). Human cases of infection by WNV (Chancey et al. 2015) are typically asymptomatic and therefore rarely reported (McDonald et al. 2021). However, some infections may develop into neuroinvasive disease, especially among certain risk groups, such as those of older age, and/or those with chronic comorbidities, such as diabetes and hypertension (Badawi et al. 2018).
Several mosquito species have been identified as being particularly efficient at transmitting the virus, because of their intrinsic vector competence, their abundance, their ornithophilic or opportunistic feeding habits and the habitat in which they live, among other factors (Reisen 2013). In North America, mosquito species of the genus Culex, such as Culex pipiens and Culex restuans, are recognized as important vectors maintaining the enzootic cycle of WNV among avian hosts. Cx. pipiens and Cx. restuans also occasionally feed on mammals, potentially bridging transmission between birds and humans (Ebel et al. 2005; Patrican et al. 2007; Hamer et al. 2008, 2009; Farajollahi et al. 2011; Andreadis 2012). Mosquitoes of the genus Aedes, such as Ae. vexans, are highly opportunistic in their choice of hosts, meaning they can also effectively bridge transmission between birds and humans (Molaei and Andreadis 2006; Greenberg et al. 2013; Anderson et al. 2020). These species are involved in transmission cycles among avian hosts in Eastern Canada and may bridge transmission to humans from early summer through to fall (Giordano et al. 2018). An association between abundance and minimum infection rates by the WNV in the two species/species groups was observed in the city of Ottawa, Canada, and was predictive of rates of WNV infection in mosquito pools (Talbot et al. 2019b), which suggests more suitable habitats for these mosquitoes may also be hot spots for WNV transmission.
Canada is currently experiencing profound climate change, and this, as well as land use change, is expected to increase the risk of zoonotic diseases, including WNV (Allan et al. 2003; Canadian Paediatric Society 2008; Chen et al. 2013; Kim et al. 2014; Gardner et al. 2014; Hoover and Barker 2016; Ludwig et al. 2019; Rakotoarinia et al. 2022). Indeed population density, landscape, climate and weather have been found to be predictive of human WNV incidence in Ontario, Canada (Giordano et al. 2017; Mallya et al. 2018). Therefore, there is a pressing need for an enhanced understanding of the environmental hazard for WNV on which to base refinements to WNV surveillance and management activities. Spatiotemporal clustering approaches have been used extensively to identify disease transmission hotspots, offering information for targeting surveillance activities (Ansari et al. 2020). Ecological niche models also offer predictive information that may be useful for public health, informing on spatial distributions of habitats most suitable for disease vectors (Peterson 2006; Escobar and Craft 2016; Escobar 2020).
Cx. pipiens/restuans demonstrate a higher preference for urban and suburban environments owing to its ability to breed in artificial containers, stagnant pools of ground water and catch basins (McMahon et al. 2008; Gardner et al. 2014; Giordano et al. 2018; Holmes and Cáceres 2020; Cloutier et al. 2021). Aedes vexans has a specifically opportunistic nature, thriving in any disturbed humid environment, including anthropogenic ones (Berec et al. 2014; Rocheleau et al. 2017; Giordano et al. 2018; Hopkins et al. 2019; Holmes and Cáceres 2020; Cloutier et al. 2021). Climatic factors have been shown to be predictive of the abundance of these species in Southeastern Canada (Wang et al. 2011; Chuang et al. 2012; Dussault et al. 2018; Ripoche et al. 2019; Rakotoarinia et al. 2022).
In this study, the ecological niche of the two main WNV vector species using data from a 10-year study period in a region of 40,000 km2 in Southeastern Canada, in the province of Ontario, was investigated. The presence of WNV-positive mosquito clusters in these data were also explored. Based on the previous literature, we hypothesized that hotspots of WNV incidence would be in areas of higher vector habitat suitability and would be located near the largest urban centers of the study area.
Materials and Methods
Study Area and Duration
The study area is defined by the following five health units of Ontario, Canada: City of Ottawa; Eastern Ontario; Leeds, Grenville and Lanark; Renfrew; and Kingston, Frontenac and Lennox and Addington, covering a total area of more than 40,000 km2. It is predominantly a forested and agricultural landscape with continental humid climate. The two major urban population centers in our study area are Ottawa and Kingston (Fig. 1).
Mosquito Collection Data
Across this study area, Public Health Ontario (PHO) collects data on mosquito surveillance at sites where a human population is present, which consists of mosquito capture and identification of 22 species/species groups. Capture is performed using light traps designed by the Centers for Disease Control and Prevention (CDC; Atlanta, GA, USA) baited with dry ice for CO2 to increase collection effectiveness. Immediately following capture, mosquitoes are stored on dry ice and transported to the laboratory for identification of species of adult female mosquitoes. Arboviral testing of certain species, prioritized by PHO according to arbovirus transmission risk, is then performed to detect WNV; females are pooled in groups of a maximum of 50 individuals of the same species from the same sampling visit, referred to as mosquito pools. Cx. pipiens/restuans is prioritized over all other species, and Ae. vexans is also of high priority. RNA is extracted from each pool using RNeasy Mini Kit (Qiagen, Hilden, Germany). RNA extracts are tested by quantitative polymerization chain reaction for arbovirus presence (Lanciotti et al. 2000).
Land Cover and Climatic Data
Land cover data for the year 2016 were obtained from Agriculture and Agri-Food Canada (AAFC) and from the United States Geological Survey (USGS) with a resolution of 30 m across our study area. Data for the year 2016 were chosen because it is approximately the midpoint of our study period from 2011 to 2020. Annual crop inventory data from AAFC (Agriculture and Agri-Food Canada 2022) comprise seven land cover classes: open water, wetlands, agricultural croplands, natural grasslands, forests, exposed surface and residential areas. Residential areas were subdivided into four categories, according to the normalized difference vegetation index (NDVI) from USGS, created using Landsat 8 data (collection 2, level 2, maximum 50% clouds) from May to October 2016, from USGS (Masek et al. 2006; Vermote et al. 2016). The goal of this procedure was to subdivide urban environments according to presence of vegetation, which may affect habitat selection by the studied species. Residential areas with NDVI < = 0.15 were referred as non-vegetated, with NDVI > 0.15 and < = 0.30 as low-green, with NDVI > 0.30 and < = 0.60 as medium-green and with NDVI > 0.60 as high-green residential areas. Data from AAFC and USGS used the same resolution with matching cell frames, and therefore, merging of the two datasets could be performed manually.
While months when mosquitoes are least active, i.e., colder months, are also when temperatures are below the threshold for WNV replication (Dohm and Turell 2001; Dohm et al. 2002; Shocket et al. 2020), temperatures and precipitations in the cold season may affect survival and adult mosquito abundance in the following warm season (Eisen et al. 2014; Roiz et al. 2014; Giménez et al. 2015). Therefore, we considered temperature and precipitation data across the entire study duration. Temperature and precipitation data were obtained from the National Aeronautics and Space Administration (NASA). Daily maximum and minimum temperature and daily total precipitation data were extracted from the daily surface weather and climatological summaries from NASA (Thornton, M.M. et al. 2020) with a resolution of 1,000 m. These data were modified to obtain average of mean daily temperature and average of total daily precipitations for the total period across our study area.
Ecological Niche Modeling Analysis
The two studied species were observed at least once in the majority of the 118 sampling locations over the study period (at only one site were both species absent and at two sites Cx. pipiens/restuans were absent). Therefore, the number of visits in which each species was observed at least once at each site was calculated, and divided by the total number of visits at each site over the study period. The resulting value is the frequency of observed presence and is a form of aggregated performance measure often used in species distribution models (Halvorsen 2012). Sites were then dichotomized into a 0/1 distribution, which is a requirement of the approach: sites with 50% or more species occurrence and sites with less than 50%. This classification informs on habitat selection behavior in the two species, rather than survival thresholds or opportunistic occurrence.
As absence and presence of a mosquito species in our dataset are likely to be affected by the same sampling bias (Phillips et al. 2009; Sillero and Barbosa 2021), the random forest algorithm was used. In general, this decision tree-based approach performs as good as the maximum entropy approach (Beeman et al. 2021; Zhao et al. 2022) and better than traditional regression-based approaches when using large datasets sampled over a long duration and a large spatial scale (Mondal and Bhat 2021). These analyses were performed using the “biomod2” package (Thuiller et al. 2009) in R 4.2.1 (R Development Core Team, Vienna, Austria). All land cover and climatic datasets were projected to Albers Conic Equal Area, which was the original projection of the annual crop inventory dataset. All explanatory variables were resampled to a cell size of 120 × 120 m and set to be at the same cell frame to reduce spatial bias caused by unequal resolution with the mosquito dataset (Sillero and Barbosa 2021). Pearson’s correlation coefficient was calculated among climatic variables at sampling sites, to identify collinearity among variables. Variables were considered collinear if r > 0.7. The prevalence parameter was set to 0.5 was specified, meaning “presence” and “absence” distributions are considered in equal proportions in the analysis (Barbet-Massin et al. 2012). For each species, 100 replicate models were trained using 80% of data. To evaluate each model, we computed a receiver operating characteristic (ROC) using the remaining 20% of data. Data was selected randomly in each model for training versus testing. The final model, trained by the 100 replicate models and using 100% of data, was used to generate a habitat suitability index (HSI) map in the study area. All other parameters were kept at default values. Models with ROC above 0.75 were used to generate an ensemble niche model (Thuiller et al. 2009). Response plots of the mean HSI across models and committee averaging (Araujo and New 2007) were generated, for each explanatory variable. Variable importance for each explanatory variable, which varies from 0 to 1, was calculated using a procedure of 100 permutations from the ensemble niche model.
Spatiotemporal Cluster Detection
Kulldorf’s spatial scan statistic implemented in the SaTScan 9.4 software (Kulldorff 1997) was used to identify possible spatiotemporal clusters with high rates of WNV-positive mosquito pools from 2011 to 2020 across the study area. The analysis was conducted separately for Cx. pipiens/restuans and Ae. vexans. The Poisson model was used, using the number of mosquitoes of the same species that were tested together as denominator. A likelihood ratio test was performed to identify clusters with significantly higher or lower relative risk than expected, defined as the observed versus expected number of cases. Data aggregation was performed for each week (seven days) of the study, which matches the maximum temporal resolution of our data, and specified 10% of the population at risk, which is recommended by the author when only specific sites in the study area were sampled, as opposed to the whole area (Kulldorff 1997). Other parameters were kept at default values. Spatial and temporal overlap of spatiotemporal clusters of WNV-positive mosquito pools were compared across the study area.
Results
Mosquito Collection Data
Sampling sites were visited at least once and at most weekly from June to September each year across the study period. A total of 45 sites (out of 118) were visited 30 times or less across the study period. Sampling sites were visited mostly during the epidemiological weeks in the period when both Cx. pipiens/restuans and Ae. vexans were most likely to be observed, i.e., weeks 24 to 40 (Fig. 2). Among sites with 30 visits or less across the study period, only one site had a visit (out of 13) outside this timeframe, specifically at week 23 of 2020. We therefore decided to keep all the data in our analysis, considering each visit had a reasonable chance to find one of the targeted species during a visit. A total of more than 110,000 Ae. vexans and more than 40,000 Cx. pipiens/restuans individuals were collected at an average of 66 sampling visits per site during the study period (Table 1). While close to three times as many Ae. vexans than Cx. pipiens/restuans individuals were collected, occurrence across sampling visits was similar between the two species (Table 1; Table S1). The number of pools tested for WNV was higher for Cx. pipiens/restuans due to higher priority of Cx. pipiens/restuans for WNV testing by PHO, and total number of positive pools was also higher for Cx. pipiens/restuans (Table 1; Table S1). However, total number of Cx. pipiens/restuans individuals tested was lower, which is due to smaller pools for that species on average compared to Ae. vexans, and minimum infection rate was much higher for Cx. pipiens/restuans (Table 1).
Land Cover and Climatic Data
Land cover consisted of a total of 10 classes. The two most abundant across the study area are the forested and agricultural cropland classes: the former being characteristic of the western part of the study area and the latter being characteristic of the eastern part (Fig. 3). All residential classes, and wetlands, grasslands and exposed classes were mostly present in the corridor between the cities of Ottawa and Kingston (Fig. 3). The open water class was mostly represented by the Ottawa and Saint Lawrence rivers, which delimit the study area in the north and south, and large waterbodies mainly in the western part of the study area (Fig. 3). Average daily total precipitation varied between 2.3 and 3.8 mm across the study area. The western part of the study area, and some areas along the Ottawa river in the north, received more precipitation on average, while areas northwest and southeast of Ottawa, and north of Kingston received less precipitations on average (Fig. 3). Average daily mean temperature varied between 3.4 and 8.5°C, and the western part of the study area was much colder on average than the eastern part, while the warmest area being the Kingston area in the south (Fig. 3). Open water, wetlands, exposed land, grasslands and low-green residential land cover classes were the least common at sampling sites, being present at fewer than 10 sampling sites (Table 2; Table S1). Habitat suitability indices computed for these land cover classes are therefore of limited use. Wetlands and exposed classes occurred at fewer than three sampling sites (Table 2). The non-vegetated residential class was extremely rare across the study area (Fig. 3) and occurred at no sampling site (Table 2). Therefore, no habitat suitability index could be computed in locations characterized by it (Fig. 4). Average total precipitations and mean temperature at cells containing a sampling site were 2.7 mm and 6.9°C, respectively (Tables 2, 3; Table S1).
Ecological Niche Modeling Analysis
The correlation coefficient between average total daily precipitation and average mean daily temperature was low (r = 0.09). Overall, receiver operating characteristic (ROC)’s area under the curve (AUC) values were much higher for Ae. vexans models compared to Cx. pipiens/restuans models. Out of 100 models, the best performing model had a ROC’s AUC = 0.77 for Cx. pipiens/restuans compared to 0.92 for Ae. vexans (Table 4). The ensemble model contained 4 and 24 models, respectively for Cx. pipiens/restuans and Ae. vexans, with a mean ROC’s AUC higher than 0.9 for mean habitat suitability index (HSI) values and committee averaging and for both species (Table 4). Explanatory variable importance of the temperature variable in the ensemble model was higher than 0.4 and higher than the importance of the other two explanatory variables. Importance of land cover and precipitation were both higher than 0.3 for Cx. pipiens/restuans and lower than 0.3 for Ae. vexans (Table 4).
Projected HSI of the best performing model for both species led to an area of highest HSI value (974 and 977 for Cx. pipiens/restuans and Ae. vexans respectively, with highest possible maximum value of 1000) concentrated in the eastern part of the study area for both species, centered on and surrounding the city of Ottawa, comprising a wide band around the city, and along the banks of the Ottawa and Saint Lawrence rivers (Fig. 4A-B). The area of highest HSI was broader for Ae. vexans than for Cx. pipiens/restuans with more suitable areas north of the city of Kingston and in the extreme eastern part of the study area, but mostly areas that were suitable for Cx. pipiens/restuans were largely also suitable for Ae. vexans (Fig. 4A-B).
Mean HSI and committee averaging showed high suitability of wetlands, croplands and the two most vegetated residential land cover classes for both species, although open water, forested and exposed classes also showed high suitability for Ae. vexans (Fig. 5A-B). Mid-range values of daily total precipitations, i.e., between roughly 2.6 and 2.8 mm, were associated with the highest suitability values, according to both mean HSI and committee averaging, for both species, with low precipitation values were associated with the lowest suitability values (Fig. 5C-D). Higher daily mean temperature values, i.e., above roughly 6°C, were associated with the highest suitability values, according to both mean HSI and committee averaging, for both species, although very high temperature values, i.e., higher than 7°C, were associated with lower suitability than upper-mid-values for Cx. pipiens/restuans (Fig. 5E–F).
Spatiotemporal Cluster Detection
A total six spatiotemporal clusters of positive pools in Cx. pipiens/restuans, and four in Ae. vexans, were observed. However, only four and one, respectively, contained significantly more observed than expected positive pools for Cx. pipiens/restuans and Ae. vexans (Table 3). One of the four significant Cx. pipiens/restuans clusters was over two months in the summer of the year 2012, and the other three clusters spanned the years 2015 to 2018 and temporally overlapped with each other (Table 3). Spatially, all four significant Cx. pipiens/restuans clusters were within and around the city of Ottawa, which is also where the highest numbers of positive pools per site were observed (Fig. 4C). The only significant Ae. vexans cluster spanned a few weeks in 2017 and was located around the city of Ottawa, which is where most positive pools were observed (Table 3; Fig. 4D).
Mean HSI was higher within spatiotemporal clusters with significantly more observed than expected positive pools (at a threshold α of 0.05) both Cx. pipiens/restuans and Ae. vexans, compared to mean HSI calculated with respect to the species across the study area (Table 5). Across sampling sites, abundance and minimum infection rates seemed to increase as HSI increased in both species (Fig. 6).
Discussion
This study’s objectives were to investigate spatiotemporal clusters of WNV-positive mosquito pools and the ecological niche of the two mosquito vector species/species groups, Cx. pipiens/restuans and Aedes vexans. We found spatiotemporal clusters of high WNV infection rates around the city of Ottawa in both mosquito vector species. We found highest habitat suitability for these same two principal vectors of WNV in the eastern half of the study area, where temperatures are generally warmer and residential and agricultural cropland cover is more dominant. Our study adds to previous results by contrasting the effect of residential land cover and vegetation cover, by including climatic variables, and by adding an analysis of Ae. vexans, which is also considered an important WNV vector species in southern Canada (Giordano et al. 2017; Anderson et al. 2020) and is generally associated with broader habitat suitability (Cloutier et al. 2021).
West Nile virus risk is often associated with suitable mosquito habitats within urban areas, such as urban wetlands and sewer overflow (Ruiz et al. 2007; Vazquez-Prokopec et al. 2010; Johnson et al. 2012). Spatiotemporal clustering analyses of West Nile virus infection in mosquitoes in North America identified proximity of rivers and water streams, high landscape fragmentation and density of roads as likely drivers of infection in mosquito pools, due to their association with WNV mosquito vector and avian reservoir abundance (Rochlin et al. 2011; Curtis et al. 2014; Myer et al. 2017). We therefore expected spatiotemporal WNV-positive mosquito pool clusters would be centered around the major urban population centers in our study area, such as found in a previous study in an area that was overlapping spatiotemporally with this study’s area, but spatially smaller, i.e., within the city limits of Ottawa, and temporally smaller and earlier in date, from 2007 to 2014. The two previously observed significant clusters included the year 2012 and were located in downtown Ottawa and one of its suburbs, and thus support one of the significant clusters detected in the study herein (Talbot et al. 2019a, 2019b). Our study’s three additional significant clusters for Cx. pipiens/restuans and one cluster for Ae. vexans occurred between 2015 and 2018, and are located in the same general area, i.e., in and around the city of Ottawa. Our results therefore add spatial and temporal depth to these previous results.
Cx. pipiens/restuans and Ae. vexans are associated with urban and/or man-modified landscapes, such as agricultural fields (Rocheleau et al. 2017; Giordano et al. 2018; Cloutier et al. 2021). Ecological niche of Cx. pipiens/restuans in a spatiotemporally overlapping area in Southeastern Ontario was previously found to be tightly linked to residential areas, namely in the large agglomerations of Ottawa and Toronto, and proximity to shrublands and forests were also associated with higher suitability (Moua et al. 2021). These results suggest that vegetated areas within urban landscapes were the most important land cover classes for Cx. pipiens/restuans. Our results support these earlier results, because we observed high habitat suitability for Cx. pipiens/restuans in residential areas with medium or high vegetation, but not with low vegetation cover. We observed similar associations for Ae. vexans. However, our results suggest areas well outside urban agglomerations, notably around agricultural croplands, are also highly suitable for both species. As in a study in Iowa (Larson et al. 2010), we found a strong association of Ae. vexans with open water and forested areas, and the suitability map was tightly linked to the two major rivers in our study area.
Previous results in Ontario did not account for climatic variables, which, according to our results, were responsible for the majority of the variation in habitat suitability index for both mosquito vector species. Temperature seemed to be limiting for both species below an annual average of 6°C and had a peak at an average of around 7°C, which may be explained by the tradeoff between optimal temperatures for host-seeking behavior, incubation and larval development (Di Pol et al. 2022). Precipitation was limiting for both species below an average of 2.5 mm and had a peak at an average of between 2.6 and 2.8 mm, which may be explained by the tradeoff between optimal rainfall for the creation of oviposition sites without flooding and flushing the larvae out of them (Chandra and Mukherjee 2022). However, variation in precipitation was low across the study area, so these effects would be best evaluated in a study area with more marked differences across sampling sites.
Our study contains one type of limitation, which concerns spatial aggregation of sampling sites around certain land cover classes, particularly around residential areas near the largest urban centers. However, the random forest algorithm and the Kulldorf’s spatial scan statistic perform well under these conditions. Therefore, we believe that most results from our niche modeling and spatiotemporal clustering analyses are reliable and meaningful.
Our study’s contributions include knowledge on the risk of West Nile virus near residential and agricultural areas demonstrated by ecological niche modeling and spatiotemporal clustering of positive mosquito pools of its two main mosquito vectors in Eastern Canada.
References
Agriculture and Agri-Food Canada (2022) Annual Crop Inventory. https://www.agr.gc.ca/atlas/apps/metrics/index-fr.html?appid=aci-iac&lang=en
Allan BF, Keesing F, Ostfeld RS (2003) Effect of forest fragmentation on Lyme disease risk. Conservation Biology 17:267–272. https://doi.org/10.1046/j.1523-1739.2003.01260.x
Anderson JF, Main AJ, Ferrandino FJ (2020) Horizontal and vertical transmission of West Nile virus by Aedes vexans (Diptera: Culicidae). Journal of Medical Entomology 57:1614–1618. https://doi.org/10.1093/jme/tjaa049
Andreadis TG (2012) The contribution of Culex pipiens complex mosquitoes to transmission and persistence of West Nile virus in North America. Journal of the American Mosquito Control Association 28:137–151. https://doi.org/10.2987/8756-971X-28.4s.137
Ansari MY, Ahmad A, Khan SS, Bhushan G, Mainuddin, (2020) Spatiotemporal clustering: a review. Artif Intell Rev 53:2381–2423. https://doi.org/10.1007/s10462-019-09736-1
Araujo M, New M (2007) Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22:42–47. https://doi.org/10.1016/j.tree.2006.09.010
Artsob H, Lindsay R, Drebot M (2006) Biodiversity-related aspects of West Nile virus and its cycle in nature. Biodiversity 7:18–23. https://doi.org/10.1080/14888386.2006.9712790
Badawi A, Velummailum R, Ryoo SG, Senthinathan A, Yaghoubi S, Vasileva D, Ostermeier E, Plishka M, Soosaipillai M, Arora P (2018) Prevalence of chronic comorbidities in dengue fever and West Nile virus: a systematic review and meta-analysis. PLoS ONE 13:e0200200. https://doi.org/10.1371/journal.pone.0200200
Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution 3:327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
Beeman SP, Morrison AM, Unnasch TR, Unnasch RS (2021) Ensemble ecological niche modeling of West Nile virus probability in Florida. PLoS ONE 16:e0256868. https://doi.org/10.1371/journal.pone.0256868
Berec L, Gelbič I, Šebesta O (2014) Worthy of their name: how floods drive outbreaks of two major floodwater mosquitoes (Diptera: Culicidae). Journal of Medical Entomology 51:76–88. https://doi.org/10.1603/ME12255
Canadian Paediatric Society (2008) West Nile virus in the context of climate change. Paediatrics & Child Health 13:399–400. https://doi.org/10.1093/pch/13.5.399
Chancey C, Grinev A, Volkova E, Rios M (2015) The global ecology and epidemiology of West Nile virus. BioMed Research International 2015:1–20. https://doi.org/10.1155/2015/376230
Chandra G, Mukherjee D (2022) Effect of climate change on mosquito population and changing pattern of some diseases transmitted by them. In: Advances in Animal Experimentation and Modeling. Elsevier, 455–460
Chen C, Jenkins E, Epp T, Waldner C, Curry P, Soos C (2013) Climate change and West Nile virus in a highly endemic region of North America. IJERPH 10:3052–3071. https://doi.org/10.3390/ijerph10073052
Chuang T-W, Ionides EL, Knepper RG, Stanuszek WW, Walker ED, Wilson ML (2012) Cross-correlation map analyses show weather variation influences on mosquito abundance patterns in Saginaw County, Michigan, 1989–2005. J Med Entomol 49:851–858. https://doi.org/10.1603/ME11150
Cloutier CA, Fyles JW, Buddle CM (2021) Diversity and community structure of mosquitoes (Diptera: Culicidae) in suburban, field, and forest habitats in Montréal, Québec, Canada. Can Entomol 153:393–411. https://doi.org/10.4039/tce.2021.8
Curtis A, Ye X, Heob E, Targhetta J, Salvato V, Reyna M, Bueno R, Holmes L (2014) A comparison of three approaches to identify West Nile virus mosquito space-time hotspots in the Houston Vicinity for the period 2002–2011. Applied Geography 51:58–64. https://doi.org/10.1016/j.apgeog.2014.02.003
Di Pol G, Crotta M, Taylor RA (2022) Modelling the temperature suitability for the risk of West Nile virus establishment in European Culex pipiens populations. Transbounding Emerging Dis 69. (Doi: https://doi.org/10.1111/tbed.14513)
Dohm DJ, O’Guinn ML, Turell MJ (2002) Effect of environmental temperature on the ability of Culex pipiens (Diptera: Culicidae) to transmit West Nile virus. J Med Entomol 39:221–225. https://doi.org/10.1603/0022-2585-39.1.221
Dohm DJ, Turell MJ (2001) Effect of incubation at overwintering temperatures on the replication of West Nile virus in New York Culex pipiens (Diptera: Culicidae). me 38:462–464. https://doi.org/10.1603/0022-2585-38.3.462
Dussault C, Nelder MP, Russell C, Johnson S, Vrbova L (2018) Evaluating the impact of Aedes japonicus invasion on the mosquito community in the Greater Golden Horseshoe region (Ontario, Canada). PLoS ONE 13:e0208911. https://doi.org/10.1371/journal.pone.0208911
Ebel GD, Rochlin I, Longacker J, Kramer LD (2005) Culex restuans (Diptera: Culicidae) relative abundance and vector competence for West Nile virus. me 42:838–843. https://doi.org/10.1603/0022-2585(2005)042[0838:CRDCRA]2.0.CO;2
Eisen L, Monaghan AJ, Lozano-Fuentes S, Steinhoff DF, Hayden MH, Bieringer PE (2014) The impact of temperature on the bionomics of Aedes (Stegomyia) aegypti, with special reference to the cool geographic range margins. J Med Entomol 51:496–516. https://doi.org/10.1603/me13214
Escobar LE (2020) Ecological niche modeling: an introduction for veterinarians and epidemiologists. Front Vet Sci 7:519059. https://doi.org/10.3389/fvets.2020.519059
Escobar LE, Craft ME (2016) Advances and limitations of disease biogeography using ecological niche modeling. Front Microbiol 07. https://doi.org/10.3389/fmicb.2016.01174
Farajollahi A, Fonseca DM, Kramer LD, Marm Kilpatrick A (2011) “Bird biting” mosquitoes and human disease: a review of the role of Culex pipiens complex mosquitoes in epidemiology. Infection, Genetics and Evolution 11:1577–1585. https://doi.org/10.1016/j.meegid.2011.08.013
Gardner AM, Lampman RL, Muturi EJ (2014) Land use patterns and the risk of West Nile virus transmission in Central Illinois. Vector-Borne and Zoonotic Diseases 14:338–345. https://doi.org/10.1089/vbz.2013.1477
Giménez JO, Fischer S, Zalazar L, Stein M (2015) Cold season mortality under natural conditions and subsequent hatching response of Aedes (Stegomyia) aegypti (Diptera: Culicidae) eggs in a subtropical city of Argentina. J Med Entomol 52:879–885. https://doi.org/10.1093/jme/tjv107
Giordano BV, Kaur S, Hunter FF (2017) West Nile virus in Ontario, Canada: a twelve-year analysis of human case prevalence, mosquito surveillance, and climate data. PLOS ONE 12:e0183568. https://doi.org/10.1371/journal.pone.0183568
Giordano B, Turner K, Hunter F (2018) Geospatial analysis and seasonal distribution of West Nile virus vectors (Diptera: Culicidae) in Southern Ontario, Canada. International Journal of Environmental Research and Public Health 15:614. https://doi.org/10.3390/ijerph15040614
Greenberg JA, Lujan DA, DiMenna MA, Wearing HJ, Hofkin BV (2013) Identification of blood meal sources in Aedes vexans and Culex quinquefasciatus in Bernalillo County, New Mexico. Journal of Insect Science 13:1–12. https://doi.org/10.1673/031.013.7501
Gubler DJ (2007) The continuing spread of West Nile virus in the western hemisphere. Clinical Infectious Diseases 45:1039–1046. https://doi.org/10.1086/521911
Halvorsen R (2012) A gradient analytic perspective on distribution modelling. Sommerfeltia 35:1–165. https://doi.org/10.2478/v10208-011-0015-3
Hamer GL, Kitron UD, Brawn JD, Loss SR, Ruiz MO, Goldberg TL, Walker ED (2008) Culex pipiens (Diptera: Culicidae): a bridge vector of West Nile virus to humans. Journal of Medical Entomology 45:125–128. https://doi.org/10.1093/jmedent/45.1.125
Hamer GL, Kitron UD, Goldberg TL, Brawn JD, Loss SR, Ruiz MO, Hayes DB, Walker ED (2009) Host selection by Culex pipiens mosquitoes and West Nile virus amplification. Am J Trop Med Hyg 80:268–278
Holmes CJ, Cáceres CE (2020) Predation differentially structures immature mosquito populations in stormwater ponds. Ecol Entomol 45:97–108. https://doi.org/10.1111/een.12783
Hoover KC, Barker CM (2016) West Nile virus, climate change, and circumpolar vulnerability. Wires Clim Change 7:283–300. https://doi.org/10.1002/wcc.382
Hopkins MC, Zink SD, Paulson SL, Hawley DM (2019) Influence of forest disturbance on La Crosse virus risk in Southwestern Virginia. InSects 11:28. https://doi.org/10.3390/insects11010028
Johnson BJ, Munafo K, Shappell L, Tsipoura N, Robson M, Ehrenfeld J, Sukhdeo MVK (2012) The roles of mosquito and bird communities on the prevalence of West Nile virus in urban wetland and residential habitats. Urban Ecosystems 15:513–531. https://doi.org/10.1007/s11252-012-0248-1
Kilpatrick AM, Daszak P, Jones MJ, Marra PP, Kramer LD (2006) Host heterogeneity dominates West Nile virus transmission. Proceedings of the Royal Society b: Biological Sciences 273:2327–2333. https://doi.org/10.1098/rspb.2006.3575
Kim BI, Blanton JD, Gilbert A, Castrodale L, Hueffer K, Slate D, Rupprecht CE (2014) A conceptual model for the impact of climate change on fox rabies in Alaska, 1980–2010. Zoonoses Public Health 61:72–80. https://doi.org/10.1111/zph.12044
Kulldorff M (1997) A spatial scan statistic. Communications in Statistics - Theory and Methods 26:1481–1496. https://doi.org/10.1080/03610929708831995
Lanciotti RS, Kerst AJ, Nasci RS, Godsey MS, Mitchell CJ, Savage HM, Komar N et al (2000) Rapid detection of West Nile virus from human clinical specimens, field-collected mosquitoes, and avian samples by a TaqMan reverse transcriptase-PCR assay. Journal of Clinical Microbiology 38(11):4066–4071
Larson SR, DeGroote JP, Bartholomay LC, Sugumaran R (2010) Ecological niche modeling of potential West Nile virus vector mosquito species in Iowa. Journal of Insect Science 10:1–17. https://doi.org/10.1673/031.010.11001
Ludwig A, Zheng H, Vrbova L, Drebot M, Iranpour M, Lindsay L (2019) Increased risk of endemic mosquito-borne diseases in Canada due to climate change. CCDR 45:91–97. https://doi.org/10.14745/ccdr.v45i04a03
Mallya S, Sander B, Roy-Gagnon M-H, Taljaard M, Jolly A, Kulkarni MA (2018) Factors associated with human West Nile virus infection in Ontario: a generalized linear mixed modelling approach. BMC Infectious Diseases 18:1–9. https://doi.org/10.1186/s12879-018-3052-6
Marini G, Rosá R, Pugliese A, Heesterbeek H (2017) Exploring vector-borne infection ecology in multi-host communities: a case study of West Nile virus. Journal of Theoretical Biology 415:58–69. https://doi.org/10.1016/j.jtbi.2016.12.009
Masek JG, Vermote EF, Saleous NE, Wolfe R, Hall FG, Huemmrich KF, Gao F, Kutler J, Lim T-K (2006) A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci Remote Sensing Lett 3:68–72. https://doi.org/10.1109/LGRS.2005.857030
McDonald E, Mathis S, Martin SW, Staples JE, Fischer M, Lindsey NP (2021) Surveillance for West Nile virus disease — United States, 2009–2018. MMWR Surveill Summ 70:1–15. https://doi.org/10.15585/mmwr.ss7001a1
McMahon TJS, Galloway TD, Anderson RA (2008) Tires as larval habitats for mosquitoes (Diptera: Culicidae) in southern Manitoba, Canada. Journal of Vector Ecology 33:198–204. https://doi.org/10.3376/1081-1710(2008)33[198:TALHFM]2.0.CO;2
Molaei G, Andreadis TG (2006) Identification of avian- and mammalian-derived bloodmeals in Aedes vexans and Culiseta melanura (Diptera: Culicidae) and its implication for West Nile virus transmission in Connecticut, U.S.A. Journal of Medical Entomology 43:1088–1093. https://doi.org/10.1603/0022-2585(2006)43[1088:IOAAMB]2.0.CO;2
Mondal R, Bhat A (2021) Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India. Ecological Indicators 129:107922. https://doi.org/10.1016/j.ecolind.2021.107922
Moua Y, Kotchi SO, Ludwig A, Brazeau S (2021) Mapping the habitat suitability of West Nile virus vectors in Southern Quebec and Eastern Ontario, Canada, with species distribution modeling and satellite earth observation data. Remote Sensing 13:1637. https://doi.org/10.3390/rs13091637
Myer MH, Campbell SR, Johnston JM (2017) Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes. Ecosphere 8. https://doi.org/10.1002/ecs2.1854
Patrican LA, Hackett LE, James E, Briggs McGowan JW, Unnasch TR, Lee J-H (2007) Host-feeding patterns of Culex mosquitoes in relation to trap habitat. Emerg Infect Dis 13:1921–1923. https://doi.org/10.3201/eid1312.070275
Peterson A (2006) Ecologic niche modeling and spatial patterns of disease transmission. Emerg Infect Dis 12:1822–1826. https://doi.org/10.3201/eid1212.060373
Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19:181–197. https://doi.org/10.1890/07-2153.1
Rakotoarinia MR, Blanchet FG, Gravel D, Lapen DR, Leighton PA, Ogden NH, Ludwig A (2022) Effects of land use and weather on the presence and abundance of mosquito-borne disease vectors in a urban and agricultural landscape in Eastern Ontario. Canada. Plos ONE 17:e0262376. https://doi.org/10.1371/journal.pone.0262376
Reisen WK (2013) Ecology of West Nile virus in North America. Viruses 5:2079–2105. https://doi.org/10.3390/v5092079
Ripoche M, Campagna C, Ludwig A, Ogden NH, Leighton PA (2019) Short-term forecasting of daily abundance of West Nile virus vectors Culex pipiens-restuans (Diptera: Culicidae) and Aedes vexans based on weather conditions in Southern Québec (Canada). Journal of Medical Entomology 56:859–872. https://doi.org/10.1093/jme/tjz002
Rocheleau JP, Michel P, Lindsay LR, Drebot M, Dibernardo A, Ogden NH, Fortin A, Arsenault J (2017) Characterizing environmental risk factors for West Nile virus in Quebec, Canada, using clinical data in humans and serology in pet dogs. Epidemiol Infect 145:2797–2807. https://doi.org/10.1017/S0950268817001625
Rochlin I, Turbow D, Gomez F, Ninivaggi DV, Campbell SR (2011) Predictive mapping of human risk for West Nile virus (WNV) based on environmental and socioeconomic factors. PLoS ONE 6:e23280. https://doi.org/10.1371/journal.pone.0023280
Roiz D, Ruiz S, Soriguer R, Figuerola J (2014) Climatic effects on mosquito abundance in Mediterranean wetlands. Parasites Vectors 7:333. https://doi.org/10.1186/1756-3305-7-333
Ruiz MO, Walker ED, Foster ES, Haramis LD, Kitron UD (2007) Association of West Nile virus illness and urban landscapes in Chicago and Detroit. International Journal of Health Geographics 6:10. https://doi.org/10.1186/1476-072X-6-10
Shocket MS, Verwillow AB, Numazu MG, Slamani H, Cohen JM, El Moustaid F, Rohr J, Johnson LR, Mordecai EA (2020) Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23°C and 26°C. eLife 9:e58511. https://doi.org/10.7554/eLife.58511
Sillero N, Barbosa AM (2021) Common mistakes in ecological niche models. International Journal of Geographical Information Science 35:213–226. https://doi.org/10.1080/13658816.2020.1798968
Smithburn KC, Hughes TP, Burke AW, Paul JH (1940) A neurotropic virus isolated from the blood of a native of Uganda. The American Journal of Tropical Medicine and Hygiene s1–20:471–492. https://doi.org/10.4269/ajtmh.1940.s1-20.471
Talbot B, Ardis M, Kulkarni MA (2019a) Influence of demography, land use, and urban form on West Nile virus risk and human West Nile virus incidence in Ottawa, Canada. Vector-Borne and Zoonotic Diseases 19:533–539. https://doi.org/10.1089/vbz.2018.2366
Talbot B, Caron-Lévesque M, Ardis M, Kryuchkov R, Kulkarni MA (2019b) Linking bird and mosquito data to assess spatiotemporal West Nile virus risk in humans. EcoHealth 16:70–81. https://doi.org/10.1007/s10393-019-01393-8
Thornton, M.M., Shrestha, R., Wei, Y., Thornton, P.E., Kao, S., Wilson, B.E. (2020) Daymet: daily surface weather data on a 1-km grid for North America, Version 4. 0 MB. https://doi.org/10.3334/ORNLDAAC/1840
Thuiller W, Lafourcade B, Engler R, Araújo MB (2009) BIOMOD - a platform for ensemble forecasting of species distributions. Ecography 32:369–373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
Vazquez-Prokopec GM, Vanden Eng JL, Kelly R, Mead DG, Kolhe P, Howgate J, Kitron U, Burkot TR (2010) The risk of West Nile virus Infection is associated with combined sewer overflow streams in urban Atlanta, Georgia, USA. Environmental Health Perspectives 118:1382–1388. https://doi.org/10.1289/ehp.1001939
Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment 185:46–56. https://doi.org/10.1016/j.rse.2016.04.008
Wang J, Ogden NH, Zhu H (2011) The impact of weather conditions on Culex pipiens and Culex restuans (Diptera: Culicidae) abundance: a case study in Peel Region. Jnl Med Entom 48:468–475. https://doi.org/10.1603/ME10117
Zhao Z, Xiao N, Shen M, Li J (2022) Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: a case study with Quasipaa boulengeri in China. Science of the Total Environment 842:156867. https://doi.org/10.1016/j.scitotenv.2022.156867
Acknowledgements
We thank Public Health Ontario (with a special mention to Curtis Russell), GDG Environment, Agriculture and Agri-Food Canada, United States Geological Survey and National Aeronautics and Space Administration for access to mosquito sampling, land cover and climatic data used in this study.
Funding
The Funding was provided by Public Health Agency of Canada and Canadian Institutes for Health Research.
Author information
Authors and Affiliations
Corresponding author
Supplementary Information
Below is the link to the electronic supplementary material.
10393_2023_1653_MOESM1_ESM.xlsx
Description of each sampling site according to occurrence (1: >50%, 0:<50%), number of WNV-positive mosquito pools, geographic coordinates, land cover at the site and average daily total precipitations (mm) and average daily mean temperature (°C) at the site. (XLSX 17 KB)
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.
About this article
Cite this article
Talbot, B., Kulkarni, M.A., Rioux-Rousseau, M. et al. Ecological Niche and Positive Clusters of Two West Nile Virus Vectors in Ontario, Canada. EcoHealth 20, 249–262 (2023). https://doi.org/10.1007/s10393-023-01653-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10393-023-01653-8