- Research
- Open Access
Critical low temperature for the survival of Aedes aegypti in Taiwan
- Pui-Jen Tsai^{1},
- Tang-Huang Lin^{2},
- Hwa-Jen Teng^{3} and
- Hsi-Chyi Yeh^{1}Email author
- Received: 8 August 2017
- Accepted: 25 December 2017
- Published: 8 January 2018
Abstract
Background
Taiwan is geographically located in a region that spans both tropical and subtropical climates (22–25°N and 120–122°E). The Taiwan Centers for Disease Control have found that the ecological habitat of Aedes aegypti appears only south of 23.5°N. Low temperatures may contribute to this particular habitat distribution of Ae. aegypti under the influence of the East Asian winter monsoon. However, the threshold condition related to critically low temperatures remains unclear because of the lack of large-scale spatial studies. This topic warrants further study, particularly through national entomological surveillance and satellite-derived land surface temperature (LST) data.
Methods
We hypothesized that the distribution of Ae. aegypti is highly correlated with the threshold nighttime LST and that a critical low LST limits the survival of Ae. aegypti. A mosquito dataset collected from the Taiwan Centers for Disease Control was utilized in conjunction with image data obtained from the moderate resolution imaging spectroradiometer (MODIS) during 2009–2011. Spatial interpolation and phi coefficient methods were used to analyze the correlation between the distributions of immature forms of Ae. aegypti and threshold LST, which was predicted from MODIS calculations for 348 townships in Taiwan.
Results
According to the evaluation of the correlation between estimated nighttime temperatures and the occurrence of Ae. aegypti, winter had the highest peak phi coefficient, and the corresponding estimated threshold temperatures ranged from 13.7 to 14 °C in the ordinary kriging model, which was the optimal interpolation model in terms of the root mean square error. The mean threshold temperature was determined to be 13.8 °C, which is a critical temperature to limit the occurrence of Ae. aegypti.
Conclusions
An LST of 13.8 °C was found to be the critical temperature for Ae. aegypti larvae, which results in the near disappearance of Ae. aegypti during winter in the subtropical regions of Taiwan under the influence of the prevailing East Asian winter monsoon.
Keywords
- Aedes aegypti
- Moderate resolution imaging spectroradiometer
- Inverse distance weighting
- Local polynomial interpolation
- Radial basis function
- Ordinary kriging
- Phi coefficient
Background
The mosquito Aedes aegypti (L.) is the primary urban vector of several globally critical arboviruses, including dengue virus (DENV) and the yellow fever virus, and is a crucial vector of chikungunya virus in the subtropics and tropics [1–6]. Bhatt et al. [7] estimated that up to 390 million DENV infections, including close to 100 million cases of dengue disease manifestations, occur annually worldwide. Aedes aegypti originated in Africa, where its ancestral form was a zoophilic tree hole mosquito Ae. aegypti formosus [8]. The “domesticated” form of Ae. aegypti is genetically distinct with discrete geographical niches [9]. A study hypothesized that harsh conditions coupled with the slave trade resulted in the introduction of Ae. aegypti from Africa to the New World, from where it subsequently spread globally to tropical and subtropical regions [8]. Dengue fever is a travel-related disease in Taiwan because travelers can carry DENV from endemic areas into the island [10–14]. After being transported to the island, this virus is passed on to Aedes mosquitoes, which can result in local dengue fever outbreaks. During 1987–2002, the epidemiological patterns of dengue in Taiwan cycled with small-scale outbreaks occurring nearly every three years and large-scale epidemics occurring nearly every ten years [15]. However, according to the data of the Taiwan Centers for Disease Control (CDC), since 2002, nine outbreaks have been recorded with over 1000 cases of dengue fever, dengue hemorrhagic fever, and dengue shock syndrome [15]. Aedes aegypti in Southern Taiwan was closely associated with a high incidence of autochthonous dengue [16].
The geographical range of Ae. aegypti is considered to be approximately within low-latitude equatorward areas with an average winter isotherm of 10 °C in the northern and southern hemispheres [17, 18]. The core distributional areas of Ae. aegypti, where the mosquito and its associated pathogens present a major threat to human health, include regions of South Asia (particularly India, Sri Lanka and Bangladesh), regions of East Asia (particularly Southern China and Taiwan), Southeast Asia, Northeastern Australia, islands in the tropical Pacific Ocean, the subtropical and tropical parts of Africa, the Caribbean islands, and large parts of the Americas with cool range margins to the North and South [18]. Taiwan is geographically located in a region that spans both tropical and subtropical climates (22–25°N and 120–122°E). The latitude of 23.5°N divides the island into two climatic zones: (i) a tropical monsoon climate in the South and (ii) a subtropical monsoon climate in the North. The latitude, topography, ocean currents, and prevailing East Asian summer monsoon over Taiwan contribute to the island’s high temperature, humidity, and rainfall, as well as tropical cyclones during summer [19]. The magnitude of the effect of increasing temperatures on the seasonality and peak abundance of mosquitoes would likely vary over the climate suitability gradient across which Ae. aegypti occurs [20]. Aedes aegypti appears only south of 23.5°N and the Penghu Islet (i.e. Penghu County) [21]. In addition, the Taiwan CDC reported similar results based on surveys of the dengue vector Ae. aegypti conducted in Taiwan during 1982, 1988–1996, 2003–2005 and 2009–2011 [22]. The temporal distribution of Ae. aegypti was positively associated with rainfall and temperature; although summer is a rainy season and winter is a dry season, the occurrence of Ae. aegypti can be observed in each season [21, 23]. Moreover, predicting the effect of changing rainfall patterns on these mosquitoes is difficult because of the complicating factor that immature forms of Ae. aegypti are found in various containers, many of which are kept filled primarily by human action rather than rainfall [24]. Therefore, in Taiwan, we consider temperature to be a prominent and significant meteorological factor affecting ecological distributions of Ae. aegypti.
Here, we proposed that a critical low winter temperature prevents the expansion of Ae. aegypti. Data on the survival-limiting temperature and development rate of Ae. aegypti are commonly obtained from laboratory experiments under artificial feeding conditions. In Taiwan, studies have estimated the developmental zero water temperatures for larvae to be from 13 to 13.9 °C; studies have also indicated that Ae. aegypti immature forms were sensitive to lower winter temperatures in the north, resulting in their limited distribution in Taiwan [25, 26]. However, supporting evidence based on a large-scale field study is still insufficient, and particular threshold temperatures for the continued absence of Ae. aegypti in Northern Taiwan remain unclear. Therefore, the goal of the current study was to estimate the critical low temperature of Ae. aegypti by using data obtained from a three-year Taiwan CDC project conducted for national entomological surveillance and using nighttime land surface temperatures (LSTs) obtained from moderate resolution imaging spectroradiometer (MODIS) observations throughout the main island of Taiwan.
Methods
Research flowchart
We hypothesized that a particular threshold temperature in winter is highly correlated with the spatial distribution of Ae. aegypti and that this low critical temperature limits the distribution of Ae. aegypti in Taiwan.
Surveillance of Aedes mosquitoes on the main island of Taiwan
Satellite-derived LST products
Spatial interpolation
Spatial interpolation is the procedure of estimating the values of an environmental variable (e.g. nighttime LST) at locations with unknown values by using a sample of locations with known values. We employed spatial interpolation involving the application of four interpolation methods to a set of points with known values to create a continuous surface. These points are also known as sample points or observations.
Inverse distance weighting
IDW estimates a value of each location by taking the distance-weighted average of the values of sample points in its neighborhood. The closer a sample point is to the location being estimated, the more influence or weight it has in the averaging process [29]. Mathematically, IDW can be expressed as follows:
The power parameter determines the significance of sample points on the interpolated value. When a higher power is defined, more emphasis is placed on nearby points, producing a more varying and less smooth surface. Specifying a lower power places more emphasis on distant points, resulting in a smoother surface. A power of 2 is most commonly used with IDW [29].
Local polynomial interpolation
A trend surface interpolation entails fitting a smooth surface defined by a polynomial function to a set of sample points and then using the polynomial function to estimate the values of unsampled locations. The general equation of a trend surface is as follows:
We considered the simplest form of a trend surface to be a planar surface with no curvature, which is defined by a local linear or first-order polynomial:
This is called a local least-squares method, which ensures that the sum of the squared deviations of observed values at sample points from the trend surface is a minimum. Suppose that there exist n sample points, whose values are z_{1}, z_{2}, …, z_{ n } and the corresponding coordinates are (x_{1}, y_{1}), (x_{2}, y_{2}), …, (x_{ n }, y_{ n }); w_{ i }is a kernel smoothing weight and is given by a negative exponential function of the following form:
Radial basis function
RBFs are conceptually similar to the approach of fitting a rubber membrane through measured sample values while minimizing the total curvature of the surface. An RBF is a real-valued function whose value depends only on the distance from the origin; the basic idea is to choose a radially symmetric function, φ(r_{ i }). The basis function selected determines how a rubber membrane would fit between the values [31]. The general form of an RBF can be written as
The form of the RBF in this study was a weighted regularized spline, which is expressed as follows:
where γ is the distance between the point and the sample, c is a smoothing parameter, and γ is Euler’s constant (γ = 0.577).
E_{1}() is an exponential integral function given by
In the regularized type, the predicted surface becomes increasingly smooth as the weight value increases [31].
Ordinary kriging
Kriging assumes that in most cases, spatial variations observed in environmental phenomena are random yet spatially correlated, and data values characterizing such phenomena conform to Tobler’s law of geography (data values at locations that are close to each other generally exhibit lower variability than data values at locations that are farther away from each other). This is called spatial autocorrelation. The exact nature of spatial autocorrelation varies with dataset and each dataset has its own unique function of variability and distance between sample points. This variability is represented by a semivariogram. Kriging uses semivariance values obtained from the fitted semivariogram to estimate the weights used in the interpolation and variance of interpolated values [29].
Semivariance measures the variability of observed values at sample points that are separated by a certain distance. It is calculated using the following equation:
OK assumes that no trend exists in the data and that the mean of the dataset is unknown. The weights are derived by solving a system of linear equations, which minimizes the expected variance of data values:
We selected the spherical semivariogram model to estimate the semivariogram that was fitted with a continuous curve.
The error variance for each interpolated point can be estimated using the following equation:
The square root of the variance provides the standard error at the interpolated point, which yields an error estimate and the confidence interval for the unknown point.
Suppose the interpolated value is z_{0}. If interpolation errors have a normal distribution, the real value at the interpolated point is within \( {z}_0\pm \left(\sqrt{\sigma^2}\times 2\right) \) with a probability of 95% [29].
Cross-validation
The cross-validation technique was adopted in this study to evaluate and compare the performance of different interpolation methods. The sample points were arbitrarily divided into two datasets, with one set used to train a model and the other used to validate the model. To reduce variability, the training and validation sets must cross over in successive rounds such that each data point could be validated against. The root mean square error (RMSE) for error measurement was estimated to evaluate the accuracy of the interpolation methods [33].
Seasonal data derived from MODIS raster images were evaluated using the four spatial interpolation methods (i.e. IDW, LPI, RBF, and OK). The interpolation methods were used to transform point data into zonal data by using ArcMap 10, and each model automatically generated the minimum, mean, and maximum estimates. The spatial interpolation models and error measurements (i.e. RMSE) applied in this study were mapped using ArcMap 10.
Transformation of the interpolated LST into 348-township maps by using the binary principle
We assumed that a critical low temperature limits the biogeographical distribution of Ae. aegypti to Southern Taiwan. Designing criteria for creating a binary map of 384 townships for LSTs was necessary; from a binary map, the extent of spatial similarity could be evaluated by comparing geographical distributions between Ae. aegypti and LSTs. The threshold temperatures were estimated to range from 8 to 28 °C, encompassing the seasonal temperatures in the plain areas of Taiwan. Criteria were designed as follows: (i) if the LST of a township was higher than an assumed temperature, then this location was considered a favorable habitat for the survival of Ae. aegypti (optimal survival conditions); (ii) if the LST of a township was lower than a given temperature assumption, then this location was considered harmful for the survival of Ae. aegypti (nonoptimal survival conditions); and (iii) according to the binary principle, optimal and nonoptimal survival conditions were defined as “1” and “0,” respectively. Scenarios from each of the given temperature assumptions were generated, which were then mapped to a dichotomous map of the 348 townships.
Phi coefficient
The phi coefficient (∅) is the version of Pearson’s γ that is used when both X and Y are true dichotomous variables [34]. It can be calculated from general Pearson’s γ by using score values of 0 and 1 or 1 and 2 for group membership variables. Alternatively, the phi coefficient can be computed from cell frequencies in a 2×2 table that summarizes the number of cases for each combination of X and Y scores. The frequencies of cases in the four cells of a 2×2 table are labeled in order to compute the phi coefficient from the cell frequencies. Assuming that the cell frequencies are from a to d (i.e. a and d correspond to “discordant” outcomes and b and c correspond to “concordant” outcomes), then the following formula can be used to compute the phi coefficient directly from the cell frequencies:
A formal significance test for the phi coefficient can be obtained by converting it into a chi-square; in the following equation, N represents the total number of scores in the contingency table:
This is a chi-square statistic with a degree of freedom (df) of 1. This can be used as one of the many possible statistics to describe relationships between categorical variables based on the tables of cell frequencies. For X^{2} with df = 1 and α = 0.05, the critical value of X^{2} is 3.84; thus, if the obtained X^{2} value exceeds 3.84, then the phi coefficient is statistically significant at the 0.05 level [34].
In this study, a correlation statistic was computed using the phi coefficient to evaluate the correlation of the binary variables, namely Ae. aegypti geographical distribution and LST, under a given temperature assumption (details described in “Transformation of the interpolated LST into 348-township maps by using the binary principle” in the Methods section). Phi coefficient statistics were calculated using SPSS 12 (SPSS, Chicago, IL, US).
Results
Descriptive statistics of the root mean square error in cross-validation of the studied interpolation models. The sample number of observations from moderate resolution imaging spectroradiometer (MODIS) was 3215. Four seasons were defined: spring (from March to May), summer (from June to August), autumn (from September to November), and winter (from December to February)
Season | IDW | LPI: exponential kernel function | RBF: completely regularized spline | OK: spherical type |
---|---|---|---|---|
Spring | 0.51 | 0.44 | 0.47 | 0.40 |
Summer | 0.60 | 0.53 | 0.51 | 0.48 |
Autumn | 0.53 | 0.46 | 0.48 | 0.42 |
Winter | 0.55 | 0.47 | 0.48 | 0.44 |
Highest values of phi coefficients demonstrating correlation between Aedes aegypti geographic distribution and threshold temperatures. The temperature assumption is estimated following the criteria mentioned in Methods. The interval of temperature assumption is 0.1 °C
Interpolation techniques | Land surface night temperature (°C) | |||
---|---|---|---|---|
Minimum | Mean | Maximum | ||
Inverse distance weighting | Threshold temperature | 13.5 | 13.8 | 14.0 |
The highest value of phi coefficient | 0.68 | 0.71 | 0.69 | |
Local polynomial interpolation: exponential kernel function | Threshold temperature | 13.8 | 13.8 | 13.9 |
The highest value of phi coefficient | 0.70 | 0.68 | 0.67 | |
Radial basis function: completely regularized spline | Threshold temperature | 13.4 | 13.8 | 14.1 |
The highest value of phi coefficient | 0.67 | 0.69 | 0.70 | |
Ordinary kriging: spherical type | Threshold temperature | 13.7 | 13.8 | 14.0 |
The highest value of phi coefficient | 0.71 | 0.69 | 0.71 |
Discussion
Developmental zero growth is based on a theoretical line, which establishes a gradient that corresponds to the growth rates at particular water temperatures in relation to immature insect forms. For Ae. aegypti, the relationship between water temperatures and development rates was similar for eggs, larvae, and pupae. Once the lower developmental zero temperature (10–14 °C) was exceeded, a near-linear relationship was observed up to 30 °C. Then, the development rate remained relatively stable or even decreased slightly before falling considerably near the upper developmental zero temperature, which occurred at 38–42 °C [20]. On the basis of the observed linear relationship between specific life stages and water temperatures, the lower developmental zero temperature has been estimated to be at 10–14 °C for eggs, 11.8–14 °C for larvae, and 10–12 °C for pupae [17, 20, 25, 35–39]. Based on the spatial similarity approach, our results indicate that the threshold land temperature ranged from 13.7 °C to 14 °C; this statistical finding is similar to those reported in previous studies evaluating the threshold water temperature for the eggs or larvae of Ae. aegypti. Moreover, the temperature extremes of 16 and 36 °C considerably decreased adult longevity and female fecundity [40]. Regarding survival time for females at extreme air temperatures, studies have reported that females uniformly survived 60 min of exposure to 39–40 °C; however, only 15–30 min of exposure to 42–43 °C resulted in 65% mortality, and 30 min of exposure to 45–51 °C was uniformly lethal [17, 41]. At extreme lower temperatures, adults could survive for 24–72 h when exposed to 4.4 °C [42] and for weeks at constant temperatures of 7–9 °C [43]. The effects of a diurnal temperature range (DTR) on female adult survival have also been shown to be considerable; an increasing DTR at a mean temperature of approximately 26 °C reduced the survival of females over an experimental period from 70% for a DTR of 0 °C to 50% for a DTR of 10 °C and 30% for a DTR of 20 °C [44]. Follow-up studies have also found that a DTR of 18.6 °C reduced female survival and reproductive output [45, 46].
In addition to temperature, water nutrient conditions, larval abundance, and the presence of biological competitors, such as Ae. albopictus, or predators also affect the growth and survival of Ae. aegypti larvae. Larvae reared in lower diet concentrations survived longer than those reared in higher diet concentrations [47]. Competition and succession in territories of coexistence between Ae. aegypti and Ae. albopictus have been reported [48–50]. However, Ae. aegypti tends to breed in urbanized ecological niches, whereas Ae. albopictus is more frequently found in rural areas [51]. Ae. aegypti prefers living indoors, whereas Ae. albopictus frequently breeds outdoors [52–55]. However, the apparent coexistence of the two species could be a transient situation, followed by a reduction [49, 56–60] or displacement [61, 62] of the resident species or satyrization [63, 64]. Furthermore, oocysts of Ascogregarina parasites (such as As. taiwanensis and As. culicis) can infect different stages of Aedes mosquito larvae, and oocyst dissemination can occur at the time of adult mosquito emergence and oviposition [65]. For example, the Ascogregarina parasite was detected in 16.7% of Ae. aegypti immature forms in temperate Argentina [66]. In the Amazon region of Brazil, the percentage of Ae. albopictus larvae infected by As. taiwanensis ranged between 21 and 93.5% and that of Ae. aegypti larvae infected by As. culicis ranged between 22 and 95% [67]. In Taiwan, field studies of Ascogregarina parasites in three coastal townships in Chiayi County (the area having the northernmost distribution of Ae. aegypti in Taiwan) reported that larvae of Aedes spp. could also be infected by Ascogregarina parasites, and the average infection rate was 4.2% in Ae. aegypti and 10.5% in Ae. albopictus in one 1994–1995 survey [68]. Socioeconomic factors affecting the distribution of Aedes mosquitoes, other than the use of containers to store water, include air conditioner use, housing quality, and urbanization rate [69, 70]. Extensive and rapid urbanization without appropriate planning may have directly resulted in large numbers of artificial containers (e.g. concrete tanks with broken lids) that are suitable for breeding Aedes mosquitoes in and around households [71–73].
According to reports from the Taiwan Malaria Research Institute, a few sporadic records of Ae. aegypti were documented in the townships of Northern Taiwan (i.e. Jhubei Township in Hsinchu County and Suao Township in Yilan County) and Central Taiwan (i.e. Nantou Township in Nantou County and a few townships in Yunlin County) during 1901–1964; however, Ae. aegypti never successfully established colonies in these townships [21]. As reported by the Central Weather Bureau [74], the surface air temperature in winter is cold in Northern Taiwan with a seasonal average of approximately 16 °C under the influence of the prevailing East Asian winter monsoon. Conversely, Southern Taiwan is very dry in winter with a seasonal mean rainfall of less than 50 mm, a trend that reverses in summer. During summer in Taiwan, the seasonal mean of the surface air temperature is nearly 28 °C, with the maximum temperature being more than 35 °C, which is magnified in urban areas, which are impacted by the East Asian Summer Monsoon, local circulation, and the urban heat-island effect. During the past century, the mean surface air temperature in Taiwan has increased by approximately 0.8 °C [74], and this may affect the distribution of Ae. aegypti in the future.
Conclusions
The critical low temperature for the survival of Ae. aegypti was statistically analyzed in this study. The phi coefficient was used to evaluate the degree of association between the ecological distributions of Ae. aegypti and the spatial distribution of seasonal nighttime temperature assumptions. The highest phi coefficient was considered to be associated with the critical low temperature for immature forms of Ae. aegypti. An LST as low as 13.8 °C in winter was considered to be the critical temperature for Ae. aegypti larvae, which could result in near disappearance of Ae. aegypti in winter in the subtropical regions of Taiwan under the influence of the prevailing East Asian winter monsoon. Because of the major role of Ae. aegypti as a vector of human pathogens, it is imperative to investigate the extent to which climate change, particularly rising temperatures and changes in rainfall patterns, affects the geographical distribution, local seasonal activity pattern, and peak abundance of Ae. aegypti, as well as the risk of human DENV infection. Our data on LST obtained using satellite telemetry can be used in mosquito control and public health management planning in the future.
Declarations
Acknowledgments
The authors thank the Taiwan Central Weather Bureau for providing access to the climate statistics database.
Funding
No funding was obtained for this study.
Availability of data and materials
All data supporting the findings are contained within the manuscript.
Authors’ contributions
PJ and HC were jointly responsible for the study design, meteorological data collection, statistical calculations, and manuscript drafting. TH assisted with MODIS LST data processing, and HJ assisted with the collection of Aedes mosquito data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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