Temperature is a key driver of transmission of mosquito-borne diseases. Both mosquitoes and the pathogens they transmit are ectotherms whose physiology and life histories depend strongly on environmental temperature (Johnson et al., 2015; Mordecai et al., 2019; Mordecai et al., 2017; Mordecai et al., 2013; Paull et al., 2017; Rogers and Randolph, 2006; Shocket et al., 2018; Tesla et al., 2018). These temperature-dependent traits drive the biological processes required for transmission. For example, temperature-dependent fecundity, development, and mortality of mosquitoes determine whether vectors are present in sufficient numbers for transmission. Temperature also affects the mosquito biting rate on hosts and probability of becoming infectious.

Mechanistic models based on these traits and guided by principles of thermal biology predict that the thermal response of transmission is unimodal: transmission peaks at intermediate temperatures and declines at extreme cold and hot temperatures (Johnson et al., 2015; Liu-Helmersson et al., 2014; Martens et al., 1997; Mordecai et al., 2019; Mordecai et al., 2017; Mordecai et al., 2013; Parham and Michael, 2010; Paull et al., 2017; Shocket et al., 2018; Tesla et al., 2018; Wesolowski et al., 2015). This unimodal response is predicted consistently across mosquito-borne diseases (Johnson et al., 2015; Mordecai et al., 2019; Mordecai et al., 2017; Mordecai et al., 2013; Paull et al., 2017; Shocket et al., 2018; Tesla et al., 2018) and supported by independent empirical evidence for positive relationships between temperature and human cases in many settings (Stewart-Ibarra and Lowe, 2013; Paull et al., 2017; Peña-García et al., 2017; Siraj et al., 2015; Werner et al., 2012), but negative relationships at high temperatures in other studies (Gatton et al., 2005; Mordecai et al., 2013; Peña-García et al., 2017; Perkins et al., 2015; Shah et al., 2019). Accordingly, we expect increasing temperatures due to climate change to shift disease distributions geographically and seasonally, as warming increases transmission in cooler settings but decreases it in settings near or above the optimal temperature for transmission (Lafferty, 2009; Lafferty and Mordecai, 2016; Rohr et al., 2011; Ryan et al., 2015). Thus, mechanistic models have provided a powerful and general rule describing how temperature affects the transmission of mosquito-borne disease. However, thermal responses vary among mosquito and pathogen species and drive important differences in how predicted transmission responds to temperature, including the specific temperatures of the optimum and thermal limits for each vector–pathogen pair (Johnson et al., 2015; Mordecai et al., 2019; Mordecai et al., 2017; Mordecai et al., 2013; Paull et al., 2017; Shocket et al., 2018; Tesla et al., 2018). We currently lack a framework to describe or predict this variation among vectors and pathogens.

Filling this gap requires comparing mechanistic, temperature-dependent transmission models for many vector–pathogen pairs. However, models that incorporate all relevant traits are not yet available for many important pairs for several reasons. First, the number of relevant vector–pathogen pairs is large because many mosquitoes transmit multiple pathogens and many pathogens are transmitted by multiple vectors. Second, empirical data are costly to produce, and existing data are often insufficient because experiments or data reporting were not designed for this purpose. Here, we address these challenges by systematically compiling data and building models for understudied mosquito-borne disease systems, including important pathogens with substantial transmission in temperate areas like West Nile virus (WNV) and Eastern Equine Encephalitis virus (EEEV). Accurately characterizing the thermal limits and optima for these systems is critical for understanding where and when temperature currently promotes or suppresses transmission and where and when climate change will increase, decrease, or have minimal effects on transmission.

In this study, we model the effects of temperature on an overlapping suite of widespread, important mosquito vectors and viruses that currently lack complete temperature-dependent models. These viruses include: West Nile virus (WNV), St. Louis Encephalitis virus (SLEV), Eastern and Western Equine Encephalitis viruses (EEEV and WEEV), Sindbis virus (SINV), and Rift Valley fever virus (RVFV) (Adouchief et al., 2016; Go et al., 2014; Kilpatrick, 2011; Linthicum et al., 2016; Weaver and Barrett, 2004; summarized in Table 1). All but RVFV sustain substantial transmission in temperate regions (Adouchief et al., 2016; Go et al., 2014; Kilpatrick, 2011; Linthicum et al., 2016; Weaver and Barrett, 2004). We selected this group because many of the viruses share common vector species and several vector species transmit multiple viruses (Table 1, Figure 1). All the viruses cause febrile illness and severe disease symptoms, including long-term arthralgia and neuroinvasive syndromes with a substantial risk of mortality in severe cases (Adouchief et al., 2016; Go et al., 2014; Kilpatrick, 2011; Linthicum et al., 2016; Weaver and Barrett, 2004). Since invading North America in 1999, WNV is now distributed worldwide (Kilpatrick, 2011; Rohr et al., 2011) and is the most common mosquito-borne disease in the US, Canada, and Europe. SLEV, EEEV, and WEEV occur in the Western hemisphere (Table 1), with cases in North, Central, and South America (Centers for Disease Control and Prevention, 2018a; Centers for Disease Control and Prevention, 2018b; Go et al., 2014). For EEEV, North American strains are genetically distinct and more virulent than the Central and South American strains (Go et al., 2014). An unusually large outbreak of EEEV in the United States in 2019 has yielded incidence four times higher than average (31 cases, resulting in nine fatalities) and brought renewed attention to this disease (Bates, 2019). SINV occurs across Europe, Africa, Asia, and Australia, with substantial transmission in northern Europe and southern Africa (Adouchief et al., 2016; Go et al., 2014). RVFV originated in eastern Africa and now also occurs across Africa and the Middle East (Linthicum et al., 2016). These pathogens primarily circulate and amplify in wild bird reservoir hosts (except RVFV, which primarily circulates in livestock). For all six viruses, humans are dead-end or unimportant reservoir hosts (Go et al., 2014; Sang et al., 2017), in contrast to pathogens like malaria, dengue virus, yellow fever virus, and Ross River virus, which sustain infection cycles between humans and mosquitoes (Go et al., 2014; Gonçalves et al., 2017; Harley et al., 2001). Most transmission of RVFV to humans occurs through direct contact with infected livestock (that are infected by mosquitoes), and to a lesser extent via the mosquito-borne transmission from infected vectors (Sang et al., 2017).

Figure 1

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We primarily focus on Culex pipiens, Cx. quinquefasciatus, and Cx. tarsalis, well-studied species that are important vectors for many of the viruses and for which appropriate temperature-dependent data exist for nearly all traits relevant to transmission. Although the closely-related Cx. pipiens and Cx. quinquefasciatus overlap in their home ranges in Africa, they have expanded into distinct regions globally (Figure 2; Farajollahi et al., 2011). Cx. pipiens occurs in higher latitude temperate areas in the Northern and Southern hemisphere, while Cx. quinquefasciatus occurs in lower latitude temperate and tropical areas (Figure 2A). By contrast, Cx. tarsalis is limited to North America but spans the tropical-temperate gradient (Figure 2B). In this system of shared pathogens and vectors with distinct geographical distributions, we also test the hypothesis that differences in thermal performance underlie variation in vector and pathogen geographic distributions, since temperate environments have cooler temperatures and a broader range of temperatures than tropical environments. We also include thermal responses from other relevant vector or laboratory model species in some models: Aedes taeniorhynchus (SINV and RVFV), Ae. triseriatus (EEEV), Ae. vexans (RVFV), Cx. theileri (RVFV), and Culiseta melanura (EEEV). Additionally, we compare our results to previously published models (Johnson et al., 2015; Mordecai et al., 2017; Mordecai et al., 2013; Shocket et al., 2018; Tesla et al., 2018) for transmission of more tropical diseases by the following vectors: Ae. aegypti, Ae. albopictus, Anopheles spp., and Cx. annulirostris.

Figure 2

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We use a mechanistic approach to characterize the effects of temperature on vector–virus pairs in this network using the thermal responses of traits that drive transmission. Specifically, we use experimental data to measure the thermal responses of the following traits: vector survival, biting rate, fecundity, development rate, competence for acquiring and transmitting each virus, and the extrinsic incubation rate of the virus within the vector. We ask: (1) Do these vectors have qualitatively similar trait thermal responses to each other, and to vectors from previous studies? (2) Is transmission of disease by these vectors predicted to be optimized and limited at similar temperatures, compared to each other and to other mosquito-borne diseases in previous studies? (3) How do the thermal responses of transmission vary across vectors that transmit the same virus and across viruses that share a vector? (4) Which traits limit transmission at low, intermediate, and high temperatures? Broadly, we hypothesize that variation in thermal responses is predictable based on vectors’ and viruses’ geographic ranges.

Mechanistic models allow us to incorporate nonlinear effects of temperature on multiple traits, measured in controlled laboratory experiments across a wide thermal gradient, to understand their combined effect on disease transmission. This approach is critical when making predictions for future climate regimes because thermal responses are almost always nonlinear, and therefore current temperature–transmission relationships may not extend into temperatures beyond those currently observed in the field. We use Bayesian inference to quantify uncertainty and to rigorously incorporate prior knowledge of mosquito thermal physiology to constrain uncertainty when data are sparse (Johnson et al., 2015). The mechanistic modeling approach also provides an independently generated, a priori prediction for the relationship between temperature and transmission to test with observational field data on human cases, allowing us to connect data across scales, from individual-level laboratory experiments, to population-level patterns of disease transmission, to climate-driven geographic variation across populations. Using this approach, we build mechanistic models for 10 vector–virus pairs by estimating thermal responses of the traits that drive transmission. We validate the models using observations of human cases in the US over space (county-level) and time (month-of-onset). The validation focuses on WNV because it is the most common of the diseases we investigated and has the most complete temperature-dependent trait data. Preliminary results of this study—the thermal responses for traits and relative R₀ models—were included in a review and synthesis article that was published last year (Mordecai et al., 2019). The present publication presents the complete methods and results, describes the vector and pathogen ecology in more detail, and provides original analyses of human case data.

As the climate changes, it is critical to understand how changes in temperature will affect the transmission of mosquito-borne diseases. Toward this goal, we developed temperature-dependent, mechanistic transmission models for 10 vector–virus pairs. The viruses—West Nile virus (WNV), St. Louis Encephalitis virus (SLEV), Eastern and Western Equine Encephalitis viruses (EEEV and WEEV), Sindbis virus (SINV), and Rift Valley fever virus (RVFV)—sustain substantial transmission in temperate areas (except RVFV), and are transmitted by shared vector species, including Cx. pipiens, Cx. quinquefasciatus, and Cx. tarsalis (except EEEV; Figure 1). Although most traits responded unimodally to temperature, as expected (Johnson et al., 2015; Mordecai et al., 2019; Mordecai et al., 2017; Mordecai et al., 2013; Shocket et al., 2018; Tesla et al., 2018), lifespan decreased linearly with temperature over the entire temperature range of available data (>14°C) for these Culex vectors (Figure 3). Transmission responded unimodally to temperature, with the thermal limits and optima for transmission varying among some of the focal mosquito and virus species (Figure 7, Table 2), largely due to differences in the thermal responses of mosquito biting rate, lifespan, vector competence, and pathogen development rate. Human case data for WNV disease across the US exhibited a strong unimodal thermal response (Figure 8), and month-of-onset data for WNV, SLEV, and EEEV were largely consistent with the predicted seasonality of transmission (Figure 9). Thus, the mechanistic models captured geographical and seasonal patterns of human incidence, despite the complexity of the enzootic cycles and spillover into humans. Our analysis was somewhat limited by the lack of data for several trait-species combinations, or by data that were sparse, particularly at high temperatures. However, our key results—maximal transmission at intermediate temperatures—are unlikely to change, and underscore the importance of considering unimodal thermal responses when predicting how climate change will impact mosquito-borne disease transmission.

The monotonically decreasing thermal responses of lifespan (lf) within the range of the available experimental data for these more temperate mosquitoes (Figure 3D) contrast with the clearly unimodal responses of more tropical species (Mordecai et al., 2019; Mordecai et al., 2017; Mordecai et al., 2013; Shocket et al., 2018). This contrast may reflect differing thermal physiology between species that use diapause or quiescence, two forms of dormancy, to persist over winter and those that do not (Diniz et al., 2017; Nelms et al., 2013; Vinogradova, 2000). Both Cx. pipiens and Cx. tarsalis diapause (Nelms et al., 2013; Vinogradova, 2000), and Cx. pipiens can survive temperatures at or near freezing (0°C) for several months (Vinogradova, 2000). Cx. quinquefasciatus enters a non-diapause quiescent state (Diniz et al., 2017; Nelms et al., 2013). Ae. albopictus, a species that occurs in both tropical and temperate zones, exhibits a latitudinal gradient in the United States in which more temperate populations diapause while sub-tropical populations do not (Urbanski et al., 2010). Experiments could test this hypothesis by measuring whether the functional form of the thermal response for lifespan differs between northern (diapausing) and southern (non-diapausing) US Ae. albopictus populations. Despite the difference in the shape of the thermal response, lifespan played a similarly important role here as in previous studies of mosquito-borne pathogens, strongly limiting transmission at high temperatures (Appendix 1—figures 11–20). Nonetheless, the thermal responses for lifespan here ultimately promote higher transmission at relatively cool temperatures because unlike in more tropical species, lifespan did not decline at cool temperatures within the range measured (>14°C). Given the lack of rigorous trait data, we cannot be certain of the shape of the thermal response of lifespan (lf) below 14°C, although it is almost certainly unimodal, especially at more extreme temperatures expected to be fatal even for diapausing mosquitoes (i.e. below 0°C). Our decision to assume lifespan (lf) plateaued at temperatures below the observed data was based on vector natural history (Vinogradova, 2000) and intended to be conservative. This approach ensured that lifespan was not a major driver of the temperature-dependence of R₀ at temperatures where it was not measured and that R₀ was instead constrained by other traits. Accordingly, our functions for lifespan (lf) do not represent the real quantitative thermal responses below the coldest observations, which limits their utility for other applications, such as predicting survival at cold temperatures and lower thermal limits on survival.

Predicted transmission for many of the diseases in this study peaked at and extended to cooler temperatures than for previously studied diseases with more tropical distributions (see Figure 7 and Table 2 for 95% credible intervals)(Mordecai et al., 2019). Here, the optimal temperatures for transmission varied from 22.7–25.2°C (excluding Ae. taeniorhynchus models, Figure 7). By contrast, models predict that transmission peaks at 25.4°C for malaria (Johnson et al., 2015; Mordecai et al., 2013), 26.4°C for Ross River virus (Shocket et al., 2018) and dengue in Ae. albopictus (Mordecai et al., 2017), 28.9°C for Zika in Ae. aegypti (Tesla et al., 2018), and 29.1°C for dengue in Ae. aegypti (Mordecai et al., 2017). Models for several vector–virus pairs also had cooler lower thermal limits (medians: 8.7–19.0°C) than those of diseases with more tropical distributions (medians: 16.0–17.8°C)(Mordecai et al., 2019). In combination with similar upper thermal limits (see below), these patterns led to wider thermal breadths (18.2–27.7°C; Figure 7) for most of the viruses here compared to the more tropical pathogens (11.7–16.7°C), excepting WNV in Cx. quinquefasciatus (12.7°C), the vector most restricted to lower latitude, sub-tropical geographic areas (Figure 2). These results match a previous finding that temperate insects had wider thermal breadths than tropical insects (Deutsch et al., 2008), and may reflect thermal adaptation to greater variation in temperature in temperate areas compared to tropical areas (Sunday et al., 2011). Additionally, SINV—a virus with substantial transmission at very high latitudes in Finland (Adouchief et al., 2016)—had the second coolest lower thermal limit (Figure 7, Table 2). Further, lower latitude Cx. quinquefasciatus outperformed higher latitude Cx. pipiens at warmer temperatures for proportion ovipositing (pO; Figure 3A), while the reverse occurred at cooler temperatures for egg viability (EV; Figure 3C). Collectively, these results imply that, to some extent, measurements of physiological traits can predict geographic patterns of vectors or disease transmission at broad scales. However, geographic range differences (Figure 2) did not consistently predict variation in thermal responses among the Culex species in this study (e.g. biting rate [a, Figure 3C] and adult lifespan [lf, Figure 3D]), indicating that life history and transmission trait responses at constant temperatures do not always predict the geographic distributions of species. Instead, the ability to tolerate temperature extremes may limit species distributions more than their performance at average or constant temperatures (Overgaard et al., 2014). Moreover, although diseases like malaria and dengue are generally considered to be ‘tropical’, historically their distributions extended further into temperate regions (Brathwaite Dick et al., 2012; Hay et al., 2004). Thus, current distributions of disease may reflect a realized niche restricted by societal factors more than a fundamental niche based on ecological factors like temperature.

In contrast to the optima, lower thermal limits, and thermal breadths, the upper thermal limits for the vector–virus pairs in this study (31.9–34.9°C, excluding Ae. taeniorhynchus models; Figure 7D, Table 2) closely matched those of more tropical diseases (31.5–34.7°C) (Johnson et al., 2015; Mordecai et al., 2017; Mordecai et al., 2013; Shocket et al., 2018; Tesla et al., 2018). This similarity may arise because maximum summer temperatures in temperate areas can match or even exceed maximum temperatures in tropical areas (Sunday et al., 2011). Accordingly, there may be a fundamental upper thermal constraint on transmission that applies similarly to all mosquito-borne diseases, driven by short mosquito lifespans at high temperatures. The relatively high upper thermal limits in both Ae. taeniorynchus transmission models were driven by the thermal response of lifespan (lf), which was fit to few data points; more data are needed to determine whether it reflects the true thermal response in that species (Appendix 1—figure 1). These results indicate that as temperatures rise due to climate change, temperate diseases are unlikely to be displaced by warming alone, although they may also expand toward the poles, even as tropical diseases may expand farther into temperate zones.

Independent human case data support unimodal thermal responses for transmission and the importance of temperature in shaping geographic patterns of mosquito-borne disease. Human cases of WNV (Ahmadnejad et al., 2016; Hahn et al., 2015; Marcantonio et al., 2015; Platonov et al., 2008; Reisen et al., 2006; Semenza et al., 2016; Shand et al., 2016) and SINV (Brummer-Korvenkontio et al., 2002; Jalava et al., 2013) are often positively associated with temperature. Here, we found that incidence of neuroinvasive WNV disease peaked at intermediate mean summer temperatures (24°C) across counties in the US (Figure 8) that matched the optima predicted by our models. This result adds to prior evidence for reduced transmission of WNV (Mallya et al., 2018) and other mosquito-borne diseases (Gatton et al., 2005; Mordecai et al., 2013; Peña-García et al., 2017; Perkins et al., 2015; Shah et al., 2019) at high temperatures. Although we did not detect lower or upper thermal limits for West Nile neuroinvasive disease (Figure 8), this result is unsurprising based on fundamental differences between the types of temperature data used to parameterize and validate the models. The R₀ model prediction is derived from data collected in a controlled laboratory environment at constant temperatures, while average incidence in the field reflects temperatures that vary at a variety of temporal scales (daily, seasonal, and interannual). Thus, we hypothesize that temperature variation over time may sustain transmission in regions with otherwise unsuitable mean summer temperatures by providing time windows that are suitable for transmission.

The temperature-dependent models also predict the general seasonality of human cases of WNV, EEEV, and SLEV (Figure 9). The 2-month lag between climate suitability and the onset of human cases, which matches previous results from other mosquito-borne diseases (Mordecai et al., 2017; Shocket et al., 2018), arises from the time following the onset of suitable conditions required for mosquito populations to increase (Stewart Ibarra et al., 2013), become infectious, and bite humans, and for humans to present symptoms and seek medical care (Hu et al., 2006; Jacups et al., 2008). Transmission of the more temperate viruses here may incur additional lags because human cases result from enzootic transmission, and multiple rounds of amplification within reservoir hosts may be required before prevalence is sufficiently high to spill over into humans. Additionally, as wild birds begin to migrate in late summer, both Cx. pipiens and Cx. tarsalis shift their feeding preferences from birds to humans, which should increase transmission to people later in the year (Kilpatrick et al., 2006). However, we found that cases decreased more quickly in autumn than expected from temperature effects alone. Human behavior may partially compensate for the shift in feeding preference and explain why the decrease of cases in autumn did not show the expected 2-month lag from temperature-dependent relative R₀. For instance, if people wear clothing that exposes less skin and spend less time outdoors due to school schedules and changing daylight it may reduce contact with mosquitoes. Drought, precipitation, and reservoir and human immunity also strongly drive transmission of WNV (Ahmadnejad et al., 2016; Marcantonio et al., 2015; Paull et al., 2017; Shand et al., 2016) and may interact with temperature. SLEV, EEEV, and WEEV are less common in nature, and thus less well-studied, but the lower thermal limits in our study support previous findings that transmission of WEEV is favored over SLEV in cooler conditions (Hess et al., 1963). Additionally, the seasonal patterns of incidence data (Figure 9) provide some support for the model prediction that SLEV transmission is possible at cooler temperatures than WNV by North American vectors (Table 2). By contrast, mean temperature is not associated with outbreaks of RVFV, although they are highly predictable based on precipitation driven by El Niño–Southern Oscillation cycles (Anyamba et al., 2009; Linthicum et al., 1999). Thus, disease dynamics depend on the interaction between temperature and other environmental factors, and the relative importance of temperature versus other drivers varies across systems.

Most prior studies with mechanistic models for temperature-dependent transmission of WNV do not capture the unimodal thermal response that our mechanistic models predict and that we observe in the human case data (Table 3). Two previous models predicted that transmission of WNV would increase up to the warmest temperatures they considered, 28°C (Vogels et al., 2017) and 35°C (Kushmaro et al., 2015). In both cases, the vector daily survival rates estimated from lab experiments were far less sensitive to temperature than our measure of adult lifespan (lf), and neither model was validated with field data. A third study with models for Cx. pipiens, Cx. quiquefasciatus, and Cx. tarsalis, like our study, predicted unimodal thermal responses for transmission, with very similar optima but with lower thermal limits that were ~5°C warmer, resulting in much narrower thermal breadths (Appendix 1—figure 23; Paull et al., 2017). This previous set of models (Paull et al., 2017) was validated with annual, state-level WNV human case data (in contrast to our county-level data averaged over multiple years), and detected a positive effect of temperature, with no decline at high temperatures (Paull et al., 2017). The best spatial and temporal scales for validating temperature-dependent transmission models and detecting the impacts of temperature remain an open question. For instance, different approaches may be necessary to detect thermal optima and thermal limits. Critically, differences in modeling and validation approaches can lead to strongly divergent conclusions and predictions for the impact of climate change.

Given the unimodal relationship between temperature and transmission of these temperate mosquito-borne pathogens, we expect climate warming to lead to predictable shifts in disease transmission (Lafferty, 2009; Lafferty and Mordecai, 2016; Ryan et al., 2015). Warming should extend the transmission season earlier into the spring and later into the fall and increase transmission potential in higher latitudes and altitudes, although this prediction may be impacted by changes in bird migrations. However, the thermal optima for these temperate vector–virus pairs are relatively cool, so in many locations, warming could result in summer temperatures that exceed the thermal optima for transmission more frequently, reducing overall transmission or creating a bimodal transmission season (Molnár et al., 2013). Based on the average summer temperature data (2001–2016) in our analysis (Figure 8), currently the majority of people (70%) and counties (68%) are below the optimal temperature for transmission (23.9°C, fit by the GAM). The numbers are similar when restricted to counties with observed West Nile virus cases: 69% and 70%, respectively. Thus, all else being equal, we might expect a net increase in transmission of West Nile virus in response to the warming climate, even as hot temperatures suppress transmission in some places. Still, warming is unlikely to eliminate any of these more temperate pathogens since the upper thermal limits for transmission are well above temperatures pathogens regularly experience in their current geographic ranges. More generally, our results raise concerns about the common practice of extrapolating monotonic relationships between temperature and disease incidence fit from observational data into warmer climate regimes to predict future cases (Marcantonio et al., 2015; Semenza et al., 2016).

While the data-driven models presented here represent the most comprehensive synthesis to date of trait thermal response data and their impact on transmission for these mosquito–pathogen systems with substantial transmission in temperate regions, additional temperature-dependent trait data would increase the accuracy and decrease the uncertainty in these models where data were sparse or missing. Our data synthesis and uncertainty analysis suggest prioritizing pathogen development rate (PDR) and vector competence (bc) data and biting rate (a) data because those thermal responses varied widely among vector–virus pairs and determined the lower thermal limits and optima for transmission in many models. Additionally, vector competence (bc) and/or pathogen development rate (PDR) data were missing in many cases (WNV in Cx. quinquefasciatus and Cx. modestus [an important vector in Europe], EEEV in Cs. melanura, RVFV in vectors from endemic areas, transmission efficiency [b] for SINV) or sparse (EEEV and WNV in Cx. univittatus), as were biting rate data (Cx. univittatus, RVFV vectors). Lifespan (lf) data—key for determining transmission optima and upper thermal limits—were the missing for Ae. triseriatus, Cs. melanura, Cx. univittatus, and RVFV vectors, and at temperatures below 14°C for all vector species, so it was unclear which functional form these thermal responses should take (monotonic, saturating, or unimodal). While the other mosquito demographic traits did not determine thermal limits for transmission in models here, fecundity (typically as eggs per female per day, EFD), larval-to-adult survival (pLA), and egg viability (EV) determined thermal limits for malaria (Mordecai et al., 2013) and Ross River virus (Shocket et al., 2018). Thus, more fecundity data (missing for Cx. tarsalis, Cx. univittatus, and Ae. triseriatus; sparse for Cx. pipiens and Cx. quinquefasciatus) would also increase our confidence in the models. New data are particularly important for RVFV: the virus has a primarily tropical distribution in Africa and the Middle East, but the model depends on traits measured in Cx. pipiens collected from temperate regions and infection traits measured in Ae. taeniorhynchus, a North American species. This substitution of a mosquito species that is not a naturally occurring vector could reduce the relevance and utility of this model. RVFV is transmitted by a diverse community of vectors across the African continent, but experiments should prioritize hypothesized primary vectors (e.g. Ae. circumluteolus or Ae. mcintoshi) or secondary vectors that already have partial trait data (e.g. Ae. vexans or Cx. theileri) (Braack et al., 2018; Linthicum et al., 2016). Although temperature itself does not predict the occurrence of RVFV outbreaks, it may affect the size of epidemics once they are triggered by precipitation. More generally, thermal responses may vary across vector populations (Kilpatrick et al., 2010) and/or virus isolates even within the same species. Several studies have found differences in thermal performance across different populations of the same mosquito species (Dodson et al., 2012; Mogi, 1992; Reisen, 1995; Ruybal et al., 2016) or pathogen strains (Kilpatrick et al., 2008), but this variation was not systematically associated with their thermal environments of origin. Accordingly, the potential for thermal adaption in mosquitoes and their pathogens remains an open question. Regardless, more data may improve the accuracy of all the models, even those without missing data.

Our trait-based R₀ models effectively isolated the physiological effects of temperature on transmission. However, in nature many other environmental and biological factors also impact transmission of mosquito-borne disease. For example, potential factors include rainfall, habitat and land-use, reservoir host community composition, host immunity, viral and mosquito genotypes, mosquito microbiome, vector control efforts, vector behavior, and human behavior (Kilpatrick et al., 2010; Kilpatrick et al., 2006; Paull et al., 2017; Shocket et al., 2020; Vaidyanathan and Scott, 2007; Vazquez-Prokopec et al., 2010). Our analyses here suggest that temperature is important for shaping broad-scale spatial and seasonal patterns of disease when cases are averaged over time and space. Other factors may be more important at finer spatial and temporal scales, and explain additional variation in human cases. For instance, a study of WNV and two other (non-mosquito-borne) pathogens found that biotic factors were significant drivers of disease distributions at local scales, while climate factors were only significant drivers at larger regional scales (Cohen et al., 2016). Given that our R₀ models for WNV predicted very similar thermal optima across three distantly-related vector species, it is likely that our results are generalizable to other temperate locations with the same vectors (e.g. parts of Europe with transmission by Cx. pipiens) at similarly broad spatial and temporal scales, even if the other factors influencing local-scale patterns are quite different than in the US.

As carbon emissions continue to increase and severe climate change becomes increasingly inevitable (Intergovernmental Panel on Climate Change, 2014), it is critical that we understand how temperature change will affect the transmission of mosquito-borne diseases in a warmer future world. While data gaps are still limiting, the mechanistic, trait-based approach presented here is powerful for predicting similarities and differences across vectors and viruses and for making predictions for the impact of climate change (Mordecai et al., 2019). Accounting for the effects of temperature variation (Bernhardt et al., 2018; Lambrechts et al., 2011; Paaijmans et al., 2010) is an important next step for using these types of models to accurately predict transmission. In nature, mosquitoes and pathogens experience daily temperature variation that can dramatically alter performance compared to constant temperatures of the same mean (Lambrechts et al., 2011; Paaijmans et al., 2010). Rate summation is the most common method for predicting performance in variable temperatures based on experimental data at constant temperatures (Bernhardt et al., 2018; Lambrechts et al., 2011). This approach is ideal because mean temperature and daily temperature variation vary somewhat independently over space and time, and measuring vector and pathogen performance at sufficient combinations of both is logistically difficult. However, its accuracy for predicting mosquito and pathogen traits or mosquito-borne disease transmission has not been rigorously evaluated. Additionally, the potential for adaptive evolution to warmer climates is uncertain because of limited knowledge on the level of genetic variation in thermal responses for vectors or their pathogens within or between populations. Further, vectors and pathogens may experience different selective pressures, as mosquito populations may depend on either increased fecundity or longevity at high temperatures, while pathogens require longer vector lifespans (Mordecai et al., 2019). Thus, future trajectories of these diseases will depend not just on suitability of mean temperatures but also on temperature variation, thermal adaptation of vectors and viruses, land use (which governs mosquito–wildlife–human interactions), vector control activities, human and wildlife immune dynamics, and potential future emergence and spread of new vectors and viruses.

All analyses were conducted using R 3.1.3 (R Development Core Team, 2016).

Data and code are available on Github (https://github.com/mshocket/Six-Viruses-Temp; copy archived at https://github.com/elifesciences-publications/Six-Viruses-Temp) and in the Dryad Data Repository.

1. 1. Shocket MS
   2. Verwillow AB
   3. Numazu MG
   4. Slamani H
   5. Cohen JM
   6. El Moustaid F
   7. Rohr J
   8. Johnson LR
   9. Mordecai EA

   (2020) Dryad

   Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23-26ºC.

   https://doi.org/10.5068/D1VW96

1. 1. Adouchief S
   2. Smura T
   3. Sane J
   4. Vapalahti O
   5. Kurkela S

   (2016) Sindbis virus as a human pathogen-epidemiology, clinical picture and pathogenesis: sindbis virus as a human pathogen

   Reviews in Medical Virology 26:221–241.

   https://doi.org/10.1002/rmv.1876

   • Google Scholar
2. 1. Ahmadnejad F
   2. Otarod V
   3. Fathnia A
   4. Ahmadabadi A
   5. Fallah MH
   6. Zavareh A
   7. Miandehi N
   8. Durand B
   9. Sabatier P

   (2016)

   Impact of climate and environmental factors on west nile virus circulation in iran

   Journal of Arthropod-Borne Diseases 10:315–327.

   • PubMed
   • Google Scholar
3. 1. Amarasekare P
   2. Savage V

   (2012) A framework for elucidating the temperature dependence of fitness

   The American Naturalist 179:178–191.

   https://doi.org/10.1086/663677

   • PubMed
   • Google Scholar
4. 1. Andreadis SS
   2. Dimotsiou OC
   3. Savopoulou-Soultani M

   (2014) Variation in adult longevity of culex pipiens f. pipiens, vector of the West Nile Virus

   Parasitology Research 113:4315–4319.

   https://doi.org/10.1007/s00436-014-4152-x

   • PubMed
   • Google Scholar
5. 1. Anyamba A
   2. Chretien JP
   3. Small J
   4. Tucker CJ
   5. Formenty PB
   6. Richardson JH
   7. Britch SC
   8. Schnabel DC
   9. Erickson RL
   10. Linthicum KJ

   (2009) Prediction of a rift valley fever outbreak

   PNAS 106:955–959.

   https://doi.org/10.1073/pnas.0806490106

   • PubMed
   • Google Scholar
6. 1. Bacaër N

   (2007) Approximation of the basic reproduction number R 0 for Vector-Borne diseases with a periodic vector population

   Bulletin of Mathematical Biology 69:1067–1091.

   https://doi.org/10.1007/s11538-006-9166-9

   • Google Scholar
7. 1. Bacaër N
   2. Ait Dads elH

   (2012) On the biological interpretation of a definition for the parameter R₀ in periodic population models

   Journal of Mathematical Biology 65:601–621.

   https://doi.org/10.1007/s00285-011-0479-4

   • PubMed
   • Google Scholar
8. 1. Bacaër N
   2. Guernaoui S

   (2006) The epidemic threshold of vector-borne diseases with seasonality: the case of cutaneous leishmaniasis in Chichaoua, Morocco

   Journal of Mathematical Biology 53:421–436.

   https://doi.org/10.1007/s00285-006-0015-0

   • PubMed
   • Google Scholar
9. Report

   1. Bates S

   (2019)

   Rare Eastern Equine Encephalitis Has Killed 9 People in the U.S. in 2019

   CNN Sans.

   • Google Scholar
10. 1. Bernhardt JR
    2. Sunday JM
    3. Thompson PL
    4. O'Connor MI

    (2018) Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment

    Proceedings of the Royal Society B: Biological Sciences 285:20181076.

    https://doi.org/10.1098/rspb.2018.1076

    • Google Scholar
11. Software

    1. Bivand R
    2. Rundel C

    (2012) rgeos: Interface to Geometry Engine - Open Source (GEOS), version 0.4-1

    R Package Version.

    http://CRAN.R-project.org/package=rgeos
12. 1. Braack L
    2. Gouveia de Almeida AP
    3. Cornel AJ
    4. Swanepoel R
    5. de Jager C

    (2018) Mosquito-borne arboviruses of African origin: review of key viruses and vectors

    Parasites & Vectors 11:9.

    https://doi.org/10.1186/s13071-017-2559-9

    • PubMed
    • Google Scholar
13. 1. Brathwaite Dick O
    2. San Martín JL
    3. Montoya RH
    4. del Diego J
    5. Zambrano B
    6. Dayan GH

    (2012) The history of dengue outbreaks in the americas

    The American Journal of Tropical Medicine and Hygiene 87:584–593.

    https://doi.org/10.4269/ajtmh.2012.11-0770

    • PubMed
    • Google Scholar
14. 1. Briere J-F
    2. Pracros P
    3. Le Roux A-Y
    4. Pierre J-S

    (1999) A novel rate model of Temperature-Dependent development for arthropods

    Environmental Entomology 28:22–29.

    https://doi.org/10.1093/ee/28.1.22

    • Google Scholar
15. 1. Brummer-Korvenkontio M
    2. Vapalahti O
    3. Kuusisto P
    4. Saikku P
    5. Manni T
    6. Koskela P
    7. Nygren T
    8. Brummer-Korvenkontio H
    9. Vaheri A

    (2002) Epidemiology of sindbis virus infections in Finland 1981-96: possible factors explaining a peculiar disease pattern

    Epidemiology and Infection 129:335–345.

    https://doi.org/10.1017/S0950268802007409

    • PubMed
    • Google Scholar
16. 1. Brust RA

    (1967) Weight and development time of different stadia of mosquitoes reared at various constant temperatures

    The Canadian Entomologist 99:986–993.

    https://doi.org/10.4039/Ent99986-9

    • Google Scholar
17. 1. Buth JL
    2. Brust RA
    3. Ellis RA

    (1990)

    Development time, oviposition activity and onset of diapause in culex tarsalis, Culex restuans and Culiseta inornata in southern manitoba

    Journal of the American Mosquito Control Association 6:55–63.

    • PubMed
    • Google Scholar
18. Report

    1. Centers for Disease Control and Prevention

    (2018a)

    St. Louis Encephalitis: Statistics and Maps

    Centers for Disease Control and Prevention.

    • Google Scholar
19. Report

    1. Centers for Disease Control and Prevention

    (2018b)

    Eastern Equine Encephalitis: Epidemiology& Geographic Distribution

    American Society for Microbiology.

    • Google Scholar
20. Report

    1. Centers for Disease Control and Prevention

    (2018c)

    West Nile Virus: Final Cumulative Maps &Data for 1999-2017

    Centers for Disease Control and Prevention.

    • Google Scholar
21. 1. Chamberlain RW
    2. Sudia WD

    (1955) The effects of temperature upon the extrinsic incubation of eastern equine encephalitis in mosquitoes

    American Journal of Hygiene 62:295–305.

    https://doi.org/10.1093/oxfordjournals.aje.a119780

    • PubMed
    • Google Scholar
22. 1. Ciota AT
    2. Matacchiero AC
    3. Kilpatrick AM
    4. Kramer LD

    (2014) The effect of temperature on life history traits of culex mosquitoes

    Journal of Medical Entomology 51:55–62.

    https://doi.org/10.1603/me13003

    • PubMed
    • Google Scholar
23. 1. Cohen JM
    2. Civitello DJ
    3. Brace AJ
    4. Feichtinger EM
    5. Ortega CN
    6. Richardson JC
    7. Sauer EL
    8. Liu X
    9. Rohr JR

    (2016) Spatial scale modulates the strength of ecological processes driving disease distributions

    PNAS 113:E3359–E3364.

    https://doi.org/10.1073/pnas.1521657113

    • PubMed
    • Google Scholar
24. 1. Cornel AJ
    2. Jupp PG
    3. Blackburn NK

    (1993) Environmental temperature on the vector competence of Culex univittatus (Diptera: Culicidae) for West Nile Virus

    Journal of Medical Entomology 30:449–456.

    https://doi.org/10.1093/jmedent/30.2.449

    • Google Scholar
25. 1. Curren EJ
    2. Lindsey NP
    3. Fischer M
    4. Hills SL

    (2018) St. Louis encephalitis virus disease in the united states, 2003-2017

    The American Journal of Tropical Medicine and Hygiene 99:1074–1079.

    https://doi.org/10.4269/ajtmh.18-0420

    • PubMed
    • Google Scholar
26. Report

    1. Darsie RF
    2. Ward RA

    (2016)

    Identification and Geographical Distribution of the Mosquitoes of North America, North of Mexico

    University Press of Florida.

    • Google Scholar
27. 1. Deutsch CA
    2. Tewksbury JJ
    3. Huey RB
    4. Sheldon KS
    5. Ghalambor CK
    6. Haak DC
    7. Martin PR

    (2008) Impacts of climate warming on terrestrial ectotherms across latitude

    PNAS 105:6668–6672.

    https://doi.org/10.1073/pnas.0709472105

    • PubMed
    • Google Scholar
28. 1. Diekmann O
    2. Heesterbeek JAP
    3. Roberts MG

    (2010) The construction of next-generation matrices for compartmental epidemic models

    Journal of the Royal Society Interface 7:873–885.

    https://doi.org/10.1098/rsif.2009.0386

    • Google Scholar
29. 1. Dietz K

    (1993) The estimation of the basic reproduction number for infectious diseases

    Statistical Methods in Medical Research 2:23–41.

    https://doi.org/10.1177/096228029300200103

    • PubMed
    • Google Scholar
30. 1. Diniz DFA
    2. de Albuquerque CMR
    3. Oliva LO
    4. de Melo-Santos MAV
    5. Ayres CFJ

    (2017) Diapause and quiescence: dormancy mechanisms that contribute to the geographical expansion of mosquitoes and their evolutionary success

    Parasites & Vectors 10:1–13.

    https://doi.org/10.1186/s13071-017-2235-0

    • PubMed
    • Google Scholar
31. 1. Dodson BL
    2. Kramer LD
    3. Rasgon JL

    (2012) Effects of larval rearing temperature on immature development and west nile virus vector competence of Culex tarsalis

    Parasites & Vectors 5:199.

    https://doi.org/10.1186/1756-3305-5-199

    • PubMed
    • Google Scholar
32. 1. Dohm DJ
    2. O'Guinn ML
    3. Turell MJ

    (2002) Effect of environmental temperature on the ability of culex pipiens (Diptera: Culicidae) to transmit West Nile virus

    Journal of Medical Entomology 39:221–225.

    https://doi.org/10.1603/0022-2585-39.1.221

    • PubMed
    • Google Scholar
33. Report

    1. European Centre for Disease Prevention and Control

    (2018)

    Surveillance and Disease Data for West Nile Fever

    European Centre for Disease Prevention and Control.

    • Google Scholar
34. 1. Farajollahi A
    2. Fonseca DM
    3. Kramer LD
    4. 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

    • Google Scholar
35. 1. Fros JJ
    2. Geertsema C
    3. Vogels CB
    4. Roosjen PP
    5. Failloux AB
    6. Vlak JM
    7. Koenraadt CJ
    8. Takken W
    9. Pijlman GP

    (2015) West nile virus: high transmission rate in North-Western european mosquitoes indicates its epidemic potential and warrants increased surveillance

    PLOS Neglected Tropical Diseases 9:e0003956.

    https://doi.org/10.1371/journal.pntd.0003956

    • PubMed
    • Google Scholar
36. 1. Gatton ML
    2. Kay BH
    3. Ryan PA

    (2005) Environmental predictors of ross river virus disease outbreaks in Queensland, Australia

    The American Journal of Tropical Medicine and Hygiene 72:792–799.

    https://doi.org/10.4269/ajtmh.2005.72.792

    • PubMed
    • Google Scholar
37. Book

    1. Gilks WR
    2. Richardson S
    3. Spiegelhalter DJ

    (1998)

    Markov Chain Monte Carlo in Practice

    Boca Raton: Chapman & Hall.

    • Google Scholar
38. 1. Go YY
    2. Balasuriya UB
    3. Lee CK

    (2014) Zoonotic encephalitides caused by arboviruses: transmission and epidemiology of alphaviruses and flaviviruses

    Clinical and Experimental Vaccine Research 3:58.

    https://doi.org/10.7774/cevr.2014.3.1.58

    • PubMed
    • Google Scholar
39. 1. Golding N
    2. Nunn MA
    3. Medlock JM
    4. Purse BV
    5. Vaux AG
    6. Schäfer SM

    (2012) West nile virus vector culex modestus established in southern England

    Parasites & Vectors 5:32.

    https://doi.org/10.1186/1756-3305-5-32

    • PubMed
    • Google Scholar
40. 1. Gonçalves BP
    2. Kapulu MC
    3. Sawa P
    4. Guelbéogo WM
    5. Tiono AB
    6. Grignard L
    7. Stone W
    8. Hellewell J
    9. Lanke K
    10. Bastiaens GJH
    11. Bradley J
    12. Nébié I
    13. Ngoi JM
    14. Oriango R
    15. Mkabili D
    16. Nyaurah M
    17. Midega J
    18. Wirth DF
    19. Marsh K
    20. Churcher TS
    21. Bejon P
    22. Sirima SB
    23. Drakeley C
    24. Bousema T

    (2017) Examining the human infectious reservoir for Plasmodium falciparum malaria in Areas of differing transmission intensity

    Nature Communications 8:1133.

    https://doi.org/10.1038/s41467-017-01270-4

    • PubMed
    • Google Scholar
41. Report

    1. Government of Canada

    (2018)

    Surveillance of West Nile Virus

    Government of Canada.

    • Google Scholar
42. 1. Hahn MB
    2. Monaghan AJ
    3. Hayden MH
    4. Eisen RJ
    5. Delorey MJ
    6. Lindsey NP
    7. Nasci RS
    8. Fischer M

    (2015) Meteorological conditions associated with increased incidence of west nile virus disease in the united states, 2004-2012

    The American Journal of Tropical Medicine and Hygiene 92:1013–1022.

    https://doi.org/10.4269/ajtmh.14-0737

    • PubMed
    • Google Scholar
43. 1. Harley D
    2. Sleigh A
    3. Ritchie S

    (2001) Ross river virus transmission, infection, and disease: a cross-disciplinary review

    Clinical Microbiology Reviews 14:909–932.

    https://doi.org/10.1128/CMR.14.4.909-932.2001

    • PubMed
    • Google Scholar
44. 1. Harris I
    2. Jones PD
    3. Osborn TJ
    4. Lister DH

    (2014) Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset: UPDATED HIGH-RESOLUTION GRIDS OF MONTHLY CLIMATIC OBSERVATIONS

    International Journal of Climatology 34:623–642.

    https://doi.org/10.1002/joc.3711

    • Google Scholar
45. 1. Hay SI
    2. Guerra CA
    3. Tatem AJ
    4. Noor AM
    5. Snow RW

    (2004) The global distribution and population at risk of malaria: past, present, and future

    The Lancet Infectious Diseases 4:327–336.

    https://doi.org/10.1016/S1473-3099(04)01043-6

    • PubMed
    • Google Scholar
46. 1. Hess AD
    2. Cherubin CE
    3. Lamotte LC

    (1963) Relation of temperature to activity of western and St. louis encephalitis viruses *

    The American Journal of Tropical Medicine and Hygiene 12:657–667.

    https://doi.org/10.4269/ajtmh.1963.12.657

    • Google Scholar
47. Software

    1. Hijmans RJ

    (2020) raster: Geographic data analysis and modeling, version 2.2.31

    R Package.

    https://rdrr.io/cran/raster/
48. 1. Hu W
    2. Tong S
    3. Mengersen K
    4. Oldenburg B

    (2006) Rainfall, mosquito density and the transmission of ross river virus: a time-series forecasting model

    Ecological Modelling 196:505–514.

    https://doi.org/10.1016/j.ecolmodel.2006.02.028

    • Google Scholar
49. 1. Huber JH
    2. Childs ML
    3. Caldwell JM
    4. Mordecai EA

    (2018) Seasonal temperature variation influences climate suitability for dengue, Chikungunya, and zika transmission

    PLOS Neglected Tropical Diseases 12:e0006451.

    https://doi.org/10.1371/journal.pntd.0006451

    • PubMed
    • Google Scholar
50. Data

    1. Huerta Jiménez H

    (authors) (2018) Actualización de la colección de artrópodos con importancia dica (CAIM), Laboratorio de entomología

    InDRE.

    https://doi.org/10.15468/y2rff2
51. Report

    1. Intergovernmental Panel on Climate Change

    (2014)

    Contribution of Working Groups I, II and III to the Fifth Assessment Climate Change: Synthesis Report

    Geneva, Switzerland: IPCC.

    • Google Scholar
52. 1. Jacups SP
    2. Whelan PI
    3. Markey PG
    4. Cleland SJ
    5. Williamson GJ
    6. Currie BJ

    (2008) Predictive indicators for ross river virus infection in the Darwin area of tropical northern Australia, using long-term mosquito trapping data

    Tropical Medicine & International Health 13:943–952.

    https://doi.org/10.1111/j.1365-3156.2008.02095.x

    • PubMed
    • Google Scholar
53. 1. Jalava K
    2. Sane J
    3. Ollgren J
    4. Ruuhela R
    5. Rätti O
    6. Kurkela S
    7. Helle P
    8. Hartonen S
    9. Pirinen P
    10. Vapalahti O
    11. Kuusi M

    (2013) Climatic, ecological and socioeconomic factors as predictors of sindbis virus infections in Finland

    Epidemiology and Infection 141:1857–1866.

    https://doi.org/10.1017/S095026881200249X

    • PubMed
    • Google Scholar
54. 1. Johnson LR
    2. Ben-Horin T
    3. Lafferty KD
    4. McNally A
    5. Mordecai E
    6. Paaijmans KP
    7. Pawar S
    8. Ryan SJ

    (2015) Understanding uncertainty in temperature effects on vector-borne disease: a bayesian approach

    Ecology 96:203–213.

    https://doi.org/10.1890/13-1964.1

    • PubMed
    • Google Scholar
55. 1. Kiarie-Makara MW
    2. Ngumbi PM
    3. Lee D-K

    (2015) Effects of temperature on the growth and development of culex pipiens Complex Mosquitoes (Diptera: Culicidae)

    Journal of Pharmacy and Biological Sciences 10:1–10.

    https://doi.org/10.9790/3008-10620110

    • Google Scholar
56. 1. Kilpatrick AM
    2. Kramer LD
    3. Jones MJ
    4. Marra PP
    5. Daszak P

    (2006) West nile virus epidemics in North America are driven by shifts in mosquito feeding behavior

    PLOS Biology 4:e82.

    https://doi.org/10.1371/journal.pbio.0040082

    • PubMed
    • Google Scholar
57. 1. Kilpatrick AM
    2. Meola MA
    3. Moudy RM
    4. Kramer LD

    (2008) Temperature, viral genetics, and the transmission of west nile virus by culex pipiens mosquitoes

    PLOS Pathogens 4:e1000092.

    https://doi.org/10.1371/journal.ppat.1000092

    • PubMed
    • Google Scholar
58. 1. Kilpatrick AM
    2. Fonseca DM
    3. Ebel GD
    4. Reddy MR
    5. Kramer LD

    (2010) Spatial and temporal variation in vector competence of culex pipiens and cx. restuans mosquitoes for west nile virus

    The American Journal of Tropical Medicine and Hygiene 83:607–613.

    https://doi.org/10.4269/ajtmh.2010.10-0005

    • PubMed
    • Google Scholar
59. 1. Kilpatrick AM

    (2011) Globalization, land use, and the invasion of west nile virus

    Science 334:323–327.

    https://doi.org/10.1126/science.1201010

    • PubMed
    • Google Scholar
60. 1. Kramer LD
    2. Hardy JL
    3. Presser SB

    (1983) Effect of temperature of extrinsic incubation on the vector competence of culex tarsalis for western equine encephalomyelitis virus

    The American Journal of Tropical Medicine and Hygiene 32:1130–1139.

    https://doi.org/10.4269/ajtmh.1983.32.1130

    • PubMed
    • Google Scholar
61. 1. Kushmaro A
    2. Friedlander TA
    3. Levins R

    (2015) Temperature effects on the basic reproductive number (R0) Of west nile virus, based on ecological parameters: endemic Vs. new emergence regions

    Journal of Tropical Diseases S1-001:.

    https://doi.org/10.4172/2329-891X.1000S1-001

    • Google Scholar
62. 1. Lafferty KD

    (2009) The ecology of climate change and infectious diseases

    Ecology 90:888–900.

    https://doi.org/10.1890/08-0079.1

    • PubMed
    • Google Scholar
63. 1. Lafferty KD
    2. Mordecai EA

    (2016) The rise and fall of infectious disease in a warmer world

    F1000Research 5:2040.

    https://doi.org/10.12688/f1000research.8766.1

    • Google Scholar
64. 1. Lambrechts L
    2. Paaijmans KP
    3. Fansiri T
    4. Carrington LB
    5. Kramer LD
    6. Thomas MB
    7. Scott TW

    (2011) Impact of daily temperature fluctuations on dengue virus transmission by aedes aegypti

    PNAS 108:7460–7465.

    https://doi.org/10.1073/pnas.1101377108

    • PubMed
    • Google Scholar
65. 1. Li J
    2. Zhu G
    3. Zhou H
    4. Tang J
    5. Cao J

    (2017)

    Effect of temperature on the development of culex pipiens pallens

    Chin J Vector Biol & Control 28:35–37.

    • Google Scholar
66. 1. Lindsey NP
    2. Staples JE
    3. Fischer M

    (2018) Eastern equine encephalitis virus in the united states, 2003-2016

    The American Journal of Tropical Medicine and Hygiene 98:1472–1477.

    https://doi.org/10.4269/ajtmh.17-0927

    • PubMed
    • Google Scholar
67. 1. Linthicum KJ
    2. Anyamba A
    3. Tucker CJ
    4. Kelley PW
    5. Myers MF
    6. Peters CJ

    (1999) Climate and satellite indicators to forecast rift valley fever epidemics in Kenya

    Science 285:397–400.

    https://doi.org/10.1126/science.285.5426.397

    • PubMed
    • Google Scholar
68. 1. Linthicum KJ
    2. Britch SC
    3. Anyamba A

    (2016) Rift valley fever: an emerging Mosquito-Borne disease

    Annual Review of Entomology 61:395–415.

    https://doi.org/10.1146/annurev-ento-010715-023819

    • PubMed
    • Google Scholar
69. 1. Liu-Helmersson J
    2. Stenlund H
    3. Wilder-Smith A
    4. Rocklöv J

    (2014) Vectorial capacity of aedes aegypti: effects of temperature and implications for global dengue epidemic potential

    PLOS ONE 9:e89783.

    https://doi.org/10.1371/journal.pone.0089783

    • PubMed
    • Google Scholar
70. 1. Loetti V
    2. Schweigmann N
    3. Burroni N

    (2011) Development rates, larval survivorship and wing length of Culex pipiens (Diptera: Culicidae) at constant temperatures

    Journal of Natural History 45:2203–2213.

    https://doi.org/10.1080/00222933.2011.590946

    • Google Scholar
71. Data

    1. López Cárdenas J

    (authors) (2018) Apoyo para la infraestructura de la colección de artrópodos con y sin importancia médica del laboratorio estatal de salud pública del estado de guanajuato

    GBIF.

    https://doi.org/10.15468/5mjcse
72. 1. Lundström JO
    2. Turell MJ
    3. Niklasson B

    (1990) Effect of environmental temperature on the vector competence of culex pipiens and cx. torrentium for Ockelbo virus

    The American Journal of Tropical Medicine and Hygiene 43:534–542.

    https://doi.org/10.4269/ajtmh.1990.43.534

    • PubMed
    • Google Scholar
73. 1. Madder DJ
    2. Surgeoner GA
    3. Helson BV

    (1983) Number of generations, egg production, and developmental time of culex pipiens and culex restauns (Diptera: culicidae) in southern ontario

    Journal of Medical Entomology 20:275–287.

    https://doi.org/10.1093/jmedent/20.3.275

    • PubMed
    • Google Scholar
74. 1. Mahmood F
    2. Crans WJ

    (1997) A thermal heat summation model to predict the duration of the gonotrophic cycle of Culiseta melanura in nature

    Journal of the American Mosquito Control Association 13:92–94.

    https://doi.org/10.1603/0022-2585(2008)45[1031:TASSOC]2.0.CO;2

    • PubMed
    • Google Scholar
75. 1. Mahmood F
    2. Crans WJ

    (1998) Effect of temperature on the development of Culiseta melanura (Diptera: culicidae) and its impact on the amplification of eastern equine encephalomyelitis virus in birds

    Journal of Medical Entomology 35:1007–1012.

    https://doi.org/10.1093/jmedent/35.6.1007

    • PubMed
    • Google Scholar
76. 1. Mallya S
    2. Sander B
    3. Roy-Gagnon MH
    4. Taljaard M
    5. Jolly A
    6. Kulkarni MA

    (2018) Factors associated with human west nile virus infection in Ontario: a generalized linear mixed modelling approach

    BMC Infectious Diseases 18:141.

    https://doi.org/10.1186/s12879-018-3052-6

    • PubMed
    • Google Scholar
77. 1. Marcantonio M
    2. Rizzoli A
    3. Metz M
    4. Rosà R
    5. Marini G
    6. Chadwick E
    7. Neteler M

    (2015) Identifying the environmental conditions favouring west nile virus outbreaks in Europe

    PLOS ONE 10:e0121158.

    https://doi.org/10.1371/journal.pone.0121158

    • PubMed
    • Google Scholar
78. 1. Martens WJM
    2. Jetten TH
    3. Focks DA

    (1997) Sensitivity of malaria, schistosomiasis and dengue to global warming

    Climatic Change 35:145–156.

    https://doi.org/10.1023/A:1005365413932

    • Google Scholar
79. 1. McHaffey DG

    (1972a) Photoperiod and temperature influences on diapause in eggs of the floodwater mosquito aedes vexans (Meigen) (Diptera: Culicidae)

    Journal of Medical Entomology 9:564–571.

    https://doi.org/10.1093/jmedent/9.6.564

    • PubMed
    • Google Scholar
80. 1. McHaffey DG

    (1972b)

    Photoperiod and temperature influences on diapause in eggs of the floodwater mosquito aedes nigromaculis (Ludlow) (Diptera : culicidae)

    Mosquito News 32:51–61.

    • Google Scholar
81. 1. McHaffey DG
    2. Harwood RF

    (1970) Photoperiod and temperature influences on diapause in eggs of the floodwater mosquito, aedes dorsalis (Meigen) (Diptera: culicidae)

    Journal of Medical Entomology 7:631–644.

    https://doi.org/10.1093/jmedent/7.6.631

    • PubMed
    • Google Scholar
82. 1. Mogi M

    (1992) Temperature and photoperiod effects on larval and ovarian development of New Zealand strains of Culex quinquefasciatus (Diptera: Culicidae)

    Annals of the Entomological Society of America 85:58–66.

    https://doi.org/10.1093/aesa/85.1.58

    • Google Scholar
83. 1. Molnár PK
    2. Kutz SJ
    3. Hoar BM
    4. Dobson AP

    (2013) Metabolic approaches to understanding climate change impacts on seasonal host-macroparasite dynamics

    Ecology Letters 16:9–21.

    https://doi.org/10.1111/ele.12022

    • PubMed
    • Google Scholar
84. 1. Mordecai EA
    2. Paaijmans KP
    3. Johnson LR
    4. Balzer C
    5. Ben-Horin T
    6. de Moor E
    7. McNally A
    8. Pawar S
    9. Ryan SJ
    10. Smith TC
    11. Lafferty KD

    (2013) Optimal temperature for malaria transmission is dramatically lower than previously predicted

    Ecology Letters 16:22–30.

    https://doi.org/10.1111/ele.12015

    • PubMed
    • Google Scholar
85. 1. Mordecai EA
    2. Cohen JM
    3. Evans MV
    4. Gudapati P
    5. Johnson LR
    6. Lippi CA
    7. Miazgowicz K
    8. Murdock CC
    9. Rohr JR
    10. Ryan SJ
    11. Savage V
    12. Shocket MS
    13. Stewart Ibarra A
    14. Thomas MB
    15. Weikel DP

    (2017) Detecting the impact of temperature on transmission of zika, dengue, and Chikungunya using mechanistic models

    PLOS Neglected Tropical Diseases 11:e0005568.

    https://doi.org/10.1371/journal.pntd.0005568

    • PubMed
    • Google Scholar
86. 1. Mordecai EA
    2. Caldwell JM
    3. Grossman MK
    4. Lippi CA
    5. Johnson LR
    6. Neira M
    7. Rohr JR
    8. Ryan SJ
    9. Savage V
    10. Shocket MS
    11. Sippy R
    12. Stewart Ibarra AM
    13. Thomas MB
    14. Villena O

    (2019) Thermal biology of mosquito-borne disease

    Ecology Letters 22:1690–1708.

    https://doi.org/10.1111/ele.13335

    • PubMed
    • Google Scholar
87. 1. Mpho M
    2. Holloway GJ
    3. Callaghan A

    (2001) A comparison of the effects of organophosphate insecticide exposure and temperature stress on fluctuating asymmetry and life history traits in culex quinquefasciatus

    Chemosphere 45:713–720.

    https://doi.org/10.1016/S0045-6535(01)00140-0

    • PubMed
    • Google Scholar
88. 1. Mpho M
    2. Callaghan A
    3. Holloway GJ

    (2002a) Temperature and genotypic effects on life history and fluctuating asymmetry in a field strain of culex pipiens

    Heredity 88:307–312.

    https://doi.org/10.1038/sj.hdy.6800045

    • Google Scholar
89. 1. Mpho M
    2. Callaghan A
    3. Holloway GJ

    (2002b) Effects of temperature and genetic stress on life history and fluctuating wing asymmetry in Culex pipiens mosquitoes

    European Journal of Entomology 99:405–412.

    https://doi.org/10.14411/eje.2002.050

    • Google Scholar
90. 1. Murdock C
    2. Evans M
    3. McClanahan T
    4. Miazgowicz K
    5. Tesla B

    (2017) Fine-scale variation in microclimate across and urban landscape changes the capacity of aedes albopictus to vector arboviruses

    PLOS Neglected Tropical Diseases 11:e0005640.

    https://doi.org/10.1371/journal.pntd.0005640

    • Google Scholar
91. 1. Muturi EJ
    2. Lampman R
    3. Costanzo K
    4. Alto BW

    (2011) Effect of temperature and insecticide stress on life-history traits of culex restuans and aedes albopictus (Diptera: culicidae)

    Journal of Medical Entomology 48:243–250.

    https://doi.org/10.1603/ME10017

    • PubMed
    • Google Scholar
92. 1. Nayar JK

    (1972) Effects of constant and fluctuating temperatures on life span of aedes taeniorhynchus adults

    Journal of Insect Physiology 18:1303–1313.

    https://doi.org/10.1016/0022-1910(72)90259-4

    • PubMed
    • Google Scholar
93. 1. Nelms BM
    2. Macedo PA
    3. Kothera L
    4. Savage HM
    5. Reisen WK

    (2013) Overwintering biology of culex (Diptera: culicidae) mosquitoes in the Sacramento valley of California

    Journal of Medical Entomology 50:773–790.

    https://doi.org/10.1603/ME12280

    • PubMed
    • Google Scholar
94. 1. Oda T
    2. Mori A
    3. Ueda M

    (1980)

    Effects of temperatures on the oviposition and hatching of eggs in culex pipiens molestus and culex pipiens quinquefasciatus

    Tropical Medicine 22:167–180.

    • Google Scholar
95. 1. Oda T
    2. Uchida K
    3. Mori A
    4. Mine M
    5. Eshita Y
    6. Kurokawa K
    7. Kato K
    8. Tahara H

    (1999)

    Effects of high temperature on the emergence and survival of adult culex pipiens molestus and Culex quinquefasciatus in Japan

    Journal of the American Mosquito Control Association 15:153–156.

    • PubMed
    • Google Scholar
96. 1. Olejnícek J
    2. Gelbic I

    (2000)

    Differences in response to temperature and density between two strains of the mosquito, culex pipiens molestus forskal

    Journal of Vector Ecology : Journal of the Society for Vector Ecology 25:136–145.

    • PubMed
    • Google Scholar
97. Data

    1. Ortega Morales AI

    (authors) (2018) Catálogo digital y clave ilustrada de los mosquitos (Diptera: culicidae) de las zonas áridas de los estados de nuevo león y Tamaulipas, méxico

    GBIF.

    https://doi.org/10.15468/otexwf
98. 1. Overgaard J
    2. Kearney MR
    3. Hoffmann AA

    (2014) Sensitivity to thermal extremes in australian Drosophila implies similar impacts of climate change on the distribution of widespread and tropical species

    Global Change Biology 20:1738–1750.

    https://doi.org/10.1111/gcb.12521

    • PubMed
    • Google Scholar
99. 1. Paaijmans KP
    2. Blanford S
    3. Bell AS
    4. Blanford JI
    5. Read AF
    6. Thomas MB

    (2010) Influence of climate on malaria transmission depends on daily temperature variation

    PNAS 107:15135–15139.

    https://doi.org/10.1073/pnas.1006422107

    • PubMed
    • Google Scholar
100. 1. Parham PE
     2. Michael E

     (2010) Modeling the effects of weather and climate change on malaria transmission

     Environmental Health Perspectives 118:620–626.

     https://doi.org/10.1289/ehp.0901256

     • PubMed
     • Google Scholar
101. 1. Parker BM

     (1982) Temperature and salinity as factors influencing the size and reproductive potentials of aedes dorsalis (Diptera: culicidae)1

     Annals of the Entomological Society of America 75:99–102.

     https://doi.org/10.1093/aesa/75.1.99

     • Google Scholar
102. 1. Paull SH
     2. Horton DE
     3. Ashfaq M
     4. Rastogi D
     5. Kramer LD
     6. Diffenbaugh NS
     7. Kilpatrick AM

     (2017) Drought and immunity determine the intensity of west nile virus epidemics and climate change impacts

     Proceedings of the Royal Society B: Biological Sciences 284:20162078.

     https://doi.org/10.1098/rspb.2016.2078

     • Google Scholar
103. 1. Peña-García VH
     2. Triana-Chávez O
     3. Arboleda-Sánchez S

     (2017) Estimating effects of temperature on dengue transmission in colombian cities

     Annals of Global Health 83:509.

     https://doi.org/10.1016/j.aogh.2017.10.011

     • PubMed
     • Google Scholar
104. 1. Perkins TA
     2. Metcalf CJ
     3. Grenfell BT
     4. Tatem AJ

     (2015) Estimating drivers of autochthonous transmission of Chikungunya virus in its invasion of the americas

     PLOS Currents 7:.

     https://doi.org/10.1371/currents.outbreaks.a4c7b6ac10e0420b1788c9767946d1fc

     • PubMed
     • Google Scholar
105. 1. Petersen LR
     2. Brault AC
     3. Nasci RS

     (2013) West Nile virus: review of the literature

     Jama 310:308.

     https://doi.org/10.1001/jama.2013.8042

     • PubMed
     • Google Scholar
106. 1. Platonov AE
     2. Fedorova MV
     3. Karan LS
     4. Shopenskaya TA
     5. Platonova OV
     6. Zhuravlev VI

     (2008) Epidemiology of west nile infection in Volgograd, Russia, in relation to climate change and mosquito (Diptera: culicidae) bionomics

     Parasitology Research 103:45–53.

     https://doi.org/10.1007/s00436-008-1050-0

     • Google Scholar
107. Conference

     1. Plummer M

     (2003)

     JAGS: a program for analysis of bayesian graphical models using gibbs sampling

     Proceedings of the 3rd International Workshop on Distributed Statistical Computing 1–10.

     • Google Scholar
108. Data

     1. Ponce García G

     (authors) (2018) Computarización de la colección de insectos y ácaros de importancia médica de la facultad de ciencias biológicas, universidad autónoma de nuevo león

     GBIF.

     https://doi.org/10.15468/sma9a5
109. Software

     1. R Development Core Team

     (2016)

     R: A language and for statistical computing

     R Foundation for Statistical Computing, Vienna, Austria.
110. 1. Rayah EAE
     2. Groun NAA

     (1983) Effect of temperature on hatching eggs and embryonic survival in the mosquito culex quinquefasciatus

     Entomologia Experimentalis Et Applicata 33:349–351.

     https://doi.org/10.1111/j.1570-7458.1983.tb03281.x

     • Google Scholar
111. 1. Reisen WK
     2. Milby MM
     3. Presser SB
     4. Hardy JL

     (1992) Ecology of mosquitoes and St. louis encephalitis virus in the Los Angeles basin of California, 1987-1990

     Journal of Medical Entomology 29:582–598.

     https://doi.org/10.1093/jmedent/29.4.582

     • PubMed
     • Google Scholar
112. 1. Reisen WK
     2. Meyer RP
     3. Presser SB
     4. Hardy JL

     (1993) Effect of temperature on the transmission of western equine encephalomyelitis and St. louis encephalitis viruses by culex tarsalis (Diptera: culicidae)

     Journal of Medical Entomology 30:151–160.

     https://doi.org/10.1093/jmedent/30.1.151

     • PubMed
     • Google Scholar
113. 1. Reisen WK

     (1995) Effect of temperature on culex tarsalis (Diptera: culicidae) from the Coachella and san joaquin valleys of California

     Journal of Medical Entomology 32:636–645.

     https://doi.org/10.1093/jmedent/32.5.636

     • PubMed
     • Google Scholar
114. 1. Reisen WK
     2. Fang Y
     3. Martinez VM

     (2006) Effects of temperature on the transmission of west nile virus by culex tarsalis (Diptera: culicidae)

     Journal of Medical Entomology 43:309–317.

     https://doi.org/10.1093/jmedent/43.2.309

     • PubMed
     • Google Scholar
115. 1. Rogers DJ
     2. Randolph SE

     (2006) Climate change and vector-borne diseases

     Advances in Parasitology 62:345–381.

     https://doi.org/10.1016/S0065-308X(05)62010-6

     • PubMed
     • Google Scholar
116. Website

     1. Rohatgi A

     (2018) WebPlotDigitizer

     Accessed July 7, 2020.

     https://automeris.io/WebPlotDigitizer/
117. 1. Rohr JR
     2. Dobson AP
     3. Johnson PT
     4. Kilpatrick AM
     5. Paull SH
     6. Raffel TR
     7. Ruiz-Moreno D
     8. Thomas MB

     (2011) Frontiers in climate change-disease research

     Trends in Ecology & Evolution 26:270–277.

     https://doi.org/10.1016/j.tree.2011.03.002

     • PubMed
     • Google Scholar
118. 1. Ronca SE
     2. Dineley KT
     3. Paessler S

     (2016) Neurological sequelae resulting from encephalitic Alphavirus infection

     Frontiers in Microbiology 7:959.

     https://doi.org/10.3389/fmicb.2016.00959

     • PubMed
     • Google Scholar
119. 1. Ronca SE
     2. Murray KO
     3. Nolan MS

     (2019) Cumulative incidence of west nile virus infection, continental united states, 1999-2016

     Emerging Infectious Diseases 25:325–327.

     https://doi.org/10.3201/eid2502.180765

     • PubMed
     • Google Scholar
120. 1. Rueda LM
     2. Patel KJ
     3. Axtell RC
     4. Stinner RE

     (1990) Temperature-dependent development and survival rates of culex quinquefasciatus and aedes aegypti (Diptera: culicidae)

     Journal of Medical Entomology 27:892–898.

     https://doi.org/10.1093/jmedent/27.5.892

     • PubMed
     • Google Scholar
121. 1. Ruybal JE
     2. Kramer LD
     3. Kilpatrick AM

     (2016) Geographic variation in the response of culex pipiens life history traits to temperature

     Parasites & Vectors 9:116.

     https://doi.org/10.1186/s13071-016-1402-z

     • PubMed
     • Google Scholar
122. 1. Ryan SJ
     2. McNally A
     3. Johnson LR
     4. Mordecai EA
     5. Ben-Horin T
     6. Paaijmans K
     7. Lafferty KD

     (2015) Mapping physiological suitability limits for malaria in africa under climate change

     Vector-Borne and Zoonotic Diseases 15:718–725.

     https://doi.org/10.1089/vbz.2015.1822

     • PubMed
     • Google Scholar
123. 1. Sang R
     2. Arum S
     3. Chepkorir E
     4. Mosomtai G
     5. Tigoi C
     6. Sigei F
     7. Lwande OW
     8. Landmann T
     9. Affognon H
     10. Ahlm C
     11. Evander M

     (2017) Distribution and abundance of key vectors of rift valley fever and other arboviruses in two ecologically distinct counties in Kenya

     PLOS Neglected Tropical Diseases 11:e0005341.

     https://doi.org/10.1371/journal.pntd.0005341

     • PubMed
     • Google Scholar
124. 1. Semenza JC
     2. Tran A
     3. Espinosa L
     4. Sudre B
     5. Domanovic D
     6. Paz S

     (2016) Climate change projections of west nile virus infections in Europe: implications for blood safety practices

     Environmental Health 15 Suppl 1:4.

     https://doi.org/10.1186/s12940-016-0105-4

     • Google Scholar
125. 1. Shah MM
     2. Krystosik AR
     3. Ndenga BA
     4. Mutuku FM
     5. Caldwell JM
     6. Otuka V
     7. Chebii PK
     8. Maina PW
     9. Jembe Z
     10. Ronga C
     11. Bisanzio D
     12. Anyamba A
     13. Damoah R
     14. Ripp K
     15. Jagannathan P
     16. Mordecai EA
     17. LaBeaud AD

     (2019) Malaria smear positivity among kenyan children peaks at intermediate temperatures as predicted by ecological models

     Parasites & Vectors 12:288.

     https://doi.org/10.1186/s13071-019-3547-z

     • PubMed
     • Google Scholar
126. 1. Shand L
     2. Brown WM
     3. Chaves LF
     4. Goldberg TL
     5. Hamer GL
     6. Haramis L
     7. Kitron U
     8. Walker ED
     9. Ruiz MO

     (2016) Predicting west nile virus infection risk from the synergistic effects of rainfall and temperature

     Journal of Medical Entomology 53:935–944.

     https://doi.org/10.1093/jme/tjw042

     • PubMed
     • Google Scholar
127. 1. Shelton RM

     (1973)

     The effect of temperatures on development of eight mosquito species

     Mosquito News 33:1–12.

     • Google Scholar
128. 1. Shocket MS
     2. Ryan SJ
     3. Mordecai EA

     (2018) Temperature explains broad patterns of ross river virus transmission

     eLife 7:e37762.

     https://doi.org/10.7554/eLife.37762

     • PubMed
     • Google Scholar
129. Book

     1. Shocket MS
     2. Anderson CB
     3. Caldwell JM
     4. Childs ML
     5. Couper LI
     6. Han S
     7. Harris MJ
     8. Howard ME
     9. Kain MP
     10. MacDonald AJ
     11. Nova N
     12. Mordecai EA

     (2020)

     Environmental Drivers of Vector-Borne Disease Population Biology of Vector-Borne Diseases

     Oxford, UK: Oxford University Press.

     • Google Scholar
130. Software

     1. Shocket MS

     (2020) Six-Viruses-Temp, version e4a5e71

     GitHub.

     https://github.com/mshocket/Six-Viruses-Temp
131. 1. Siraj AS
     2. Bouma MJ
     3. Santos-Vega M
     4. Yeshiwondim AK
     5. Rothman DS
     6. Yadeta D
     7. Sutton PC
     8. Pascual M

     (2015) Temperature and population density determine reservoir regions of seasonal persistence in Highland malaria

     Proceedings of the Royal Society B: Biological Sciences 282:20151383.

     https://doi.org/10.1098/rspb.2015.1383

     • Google Scholar
132. 1. Smith JL
     2. Fonseca DM

     (2004) Rapid assays for identification of members of the culex (Culex) pipiens complex, their hybrids, and other sibling species (Diptera: culicidae)

     The American Journal of Tropical Medicine and Hygiene 70:339–345.

     https://doi.org/10.4269/ajtmh.2004.70.339

     • PubMed
     • Google Scholar
133. 1. Spiegelhalter DJ
     2. Best NG
     3. Carlin BP
     4. van der Linde A

     (2002) Bayesian measures of model complexity and fit

     Journal of the Royal Statistical Society: Series B 64:583–639.

     https://doi.org/10.1111/1467-9868.00353

     • Google Scholar
134. Software

     1. Spiegelhalter D
     2. Thomas A
     3. Best N
     4. Lunn D

     (2003) WinBUGS User Manual, version 1.4

     WinBUGS User Manual.

     https://www.mrc-bsu.cam.ac.uk/wp-content/uploads/manual14.pdf
135. 1. Stewart Ibarra AM
     2. Ryan SJ
     3. Beltrán E
     4. Mejía R
     5. Silva M
     6. Muñoz A

     (2013) Dengue vector dynamics (aedes aegypti) influenced by climate and social factors in Ecuador: implications for targeted control

     PLOS ONE 8:e78263.

     https://doi.org/10.1371/journal.pone.0078263

     • PubMed
     • Google Scholar
136. 1. Stewart-Ibarra AM
     2. Lowe R

     (2013) Climate and Non-Climate drivers of dengue epidemics in southern coastal Ecuador

     The American Journal of Tropical Medicine and Hygiene 88:971–981.

     https://doi.org/10.4269/ajtmh.12-0478

     • PubMed
     • Google Scholar
137. Software

     1. Su Y-S
     2. Yajima M

     (2009) R2jags: A Package for Running Jags From R, version 0.6-1

     R2jags: A Package for Running Jags From R.

     https://rdrr.io/cran/R2jags/
138. 1. Sunday JM
     2. Bates AE
     3. Dulvy NK

     (2011) Global analysis of thermal tolerance and latitude in ectotherms

     Proceedings of the Royal Society B: Biological Sciences 278:1823–1830.

     https://doi.org/10.1098/rspb.2010.1295

     • Google Scholar
139. 1. Tekle A

     (1960) The Physiology of Hibernation and Its Role in the Geographical Distribution of Populations of the Culex Pipiens Complex

     The American Journal of Tropical Medicine and Hygiene 9:321–330.

     https://doi.org/10.4269/ajtmh.1960.9.321

     • Google Scholar
140. 1. Teng HJ
     2. Apperson CS

     (2000) Development and survival of immature aedes albopictus and aedes triseriatus (Diptera: culicidae) in the laboratory: effects of density, food, and competition on response to temperature

     Journal of Medical Entomology 37:40–52.

     https://doi.org/10.1603/0022-2585-37.1.40

     • PubMed
     • Google Scholar
141. 1. Tesla B
     2. Demakovsky LR
     3. Mordecai EA
     4. Ryan SJ
     5. Bonds MH
     6. Ngonghala CN
     7. Brindley MA
     8. Murdock CC

     (2018) Temperature drives zika virus transmission: evidence from empirical and mathematical models

     Proceedings of the Royal Society B: Biological Sciences 285:20180795.

     https://doi.org/10.1098/rspb.2018.0795

     • Google Scholar
142. 1. Trpiš M
     2. Shemanchuk JA

     (1970) Effect of constant temperature on the larval development of aedes vexans (diptera: culicidae)

     The Canadian Entomologist 102:1048–1051.

     https://doi.org/10.4039/Ent1021048-8

     • Google Scholar
143. 1. Turell MJ
     2. Rossi CA
     3. Bailey CL

     (1985) Effect of extrinsic incubation temperature on the ability of aedes taeniorhynchus and culex pipiens to transmit rift valley fever virus

     The American Journal of Tropical Medicine and Hygiene 34:1211–1218.

     https://doi.org/10.4269/ajtmh.1985.34.1211

     • PubMed
     • Google Scholar
144. 1. Turell MJ
     2. Lundström JO

     (1990) Effect of environmental temperature on the vector competence of aedes aegypti and ae. taeniorhynchus for ockelbo virus

     The American Journal of Tropical Medicine and Hygiene 43:543–550.

     https://doi.org/10.4269/ajtmh.1990.43.543

     • PubMed
     • Google Scholar
145. 1. Urbanski JM
     2. Benoit JB
     3. Michaud MR
     4. Denlinger DL
     5. Armbruster P

     (2010) The molecular physiology of increased egg desiccation resistance during diapause in the invasive mosquito, Aedes albopictus

     Proceedings of the Royal Society B: Biological Sciences 277:2683–2692.

     https://doi.org/10.1098/rspb.2010.0362

     • Google Scholar
146. 1. Vaidyanathan R
     2. Scott TW

     (2007) Geographic variation in vector competence for west nile virus in the culex pipiens (Diptera: culicidae) complex in California

     Vector-Borne and Zoonotic Diseases 7:193–198.

     https://doi.org/10.1089/vbz.2006.0589

     • PubMed
     • Google Scholar
147. 1. van der Linde TC de K
     2. Hewitt PH
     3. Nel A
     4. van der Westhuizen MC

     (1990)

     Development rates and percentage hatching of culex (Culex) theileri; Theobald (Diptera: culicidae) eggs at various constant temperatures

     Journal of the Entomological Society of Southern Africa 53:17–26.

     • Google Scholar
148. 1. Vazquez-Prokopec GM
     2. Vanden Eng JL
     3. Kelly R
     4. Mead DG
     5. Kolhe P
     6. Howgate J
     7. Kitron U
     8. 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

     • PubMed
     • Google Scholar
149. Book

     1. Venables WN
     2. Ripley BD

     (2002) Modern Applied Statistics with S, Statistics and Computing

     New York: Springer.

     https://doi.org/10.1007/978-0-387-21706-2

     • Google Scholar
150. Book

     1. Vinogradova EB

     (2000)

     Culex Pipiens Pipiens Mosquitoes: Taxonomy, Distribution, Ecology, Physiology, Genetics, Applied Importance and Control

     Pensoft Publishers.

     • Google Scholar
151. 1. Vogels CBF
     2. Fros JJ
     3. Göertz GP
     4. Pijlman GP
     5. Koenraadt CJM

     (2016) Vector competence of northern european culex pipiens biotypes and hybrids for west nile virus is differentially affected by temperature

     Parasites & Vectors 9:1677.

     https://doi.org/10.1186/s13071-016-1677-0

     • Google Scholar
152. 1. Vogels CBF
     2. Hartemink N
     3. Koenraadt CJM

     (2017) Modelling west nile virus transmission risk in Europe: effect of temperature and mosquito biotypes on the basic reproduction number

     Scientific Reports 7:4.

     https://doi.org/10.1038/s41598-017-05185-4

     • PubMed
     • Google Scholar
153. Data

     1. Walter Reed Biosystematics Unit

     (authors) (2018) Mosquito occurrence dataset

     GBIF.

     https://doi.org/10.15468/tw2y9b
154. 1. Weaver SC
     2. Barrett AD

     (2004) Transmission cycles, host range, evolution and emergence of arboviral disease

     Nature Reviews Microbiology 2:789–801.

     https://doi.org/10.1038/nrmicro1006

     • PubMed
     • Google Scholar
155. 1. Werner AK
     2. Goater S
     3. Carver S
     4. Robertson G
     5. Allen GR
     6. Weinstein P

     (2012) Environmental drivers of ross river virus in southeastern Tasmania, Australia: towards strengthening public health interventions

     Epidemiology and Infection 140:359–371.

     https://doi.org/10.1017/S0950268811000446

     • PubMed
     • Google Scholar
156. 1. Wesolowski A
     2. Qureshi T
     3. Boni MF
     4. Sundsøy PR
     5. Johansson MA
     6. Rasheed SB
     7. Engø-Monsen K
     8. Buckee CO

     (2015) Impact of human mobility on the emergence of dengue epidemics in Pakistan

     PNAS 112:11887–11892.

     https://doi.org/10.1073/pnas.1504964112

     • PubMed
     • Google Scholar
157. Book

     1. Wood SN

     (2006)

     Generalized Additive Models: An Introduction with R, Texts in Statistical Science

     Boca Raton, FL: Chapman & Hall/CRC.

     • Google Scholar
158. Report

     1. World Health Organization

     (2018)

     Rift Valley Fever Fact Sheet

     World Health Organization.

     • Google Scholar
159. Report

     1. World Meteorological Organization

     (2009)

     Handbook on CLIMAT and CLIMAT TEMP Reporting, 2009 Edition

     World Meteorological Organization.

     • Google Scholar