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Long-term spatio-temporal dynamics of the mosquito Aedes aegypti in temperate Argentina

Published online by Cambridge University Press:  23 November 2016

S. Fischer*
Affiliation:
Departamento de Ecología, Genética y Evolución, and IEGEBA (UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
M.S. De Majo
Affiliation:
Departamento de Ecología, Genética y Evolución, and IEGEBA (UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
L. Quiroga
Affiliation:
Departamento de Ecología, Genética y Evolución, and IEGEBA (UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
M. Paez
Affiliation:
Departamento de Ecología, Genética y Evolución, and IEGEBA (UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
N. Schweigmann
Affiliation:
Departamento de Ecología, Genética y Evolución, and IEGEBA (UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
*
*Author for correspondence Phone: +54-11-4576-3300/09 (int. 364) Fax: +54-11-4576-3372 E-mail: sylvia@ege.fcen.uba.ar
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Abstract

Buenos Aires city is located near the southern limit of the distribution of Aedes aegypti (Diptera: Culicidae). This study aimed to assess long-term variations in the abundance of Ae. aegypti in Buenos Aires in relation to changes in climatic conditions. Ae. aegypti weekly oviposition activity was analyzed and compared through nine warm seasons from 1998 to 2014, with 200 ovitraps placed across the whole extension of the city. The temporal and spatial dynamics of abundances were compared among seasons, and their relation with climatic variables were analyzed. Results showed a trend to higher peak abundances, a higher number of infested sites, and longer duration of the oviposition season through subsequent years, consistent with a long-term colonization process. In contrast, thermal favorability and rainfall pattern did not show a consistent trend of changes. The long-term increase in abundance, and the recently documented expansion of Ae. aegypti to colder areas of Buenos Aires province suggest that local populations might be adapting to lower temperature conditions. The steadily increasing abundances may have implications on the risk of dengue transmission.

Type
Research Papers
Copyright
Copyright © Cambridge University Press 2016 

Introduction

Aedes (=Stegomyia) aegypti (Diptera: Culicidae) is a species originally from West Africa, which was introduced into the Americas with the first arrival of Europeans, most likely transported by slave ships in water-holding containers. This species is considered invasive based on its tremendous potential to spread to new environments (Lounibos, Reference Lounibos2002). Other traits associated with its invasion success are the desiccation-resistant eggs, installment hatching, and the behavior of females to distribute eggs in several containers, all of which ensure that at least part of the offspring survives and reproduces, and facilitate the establishment in recently colonized areas (Becker et al., Reference Becker, Pluskota, Kaiser, Schaffner and Melhorn2012).

Although the presence of Ae. aegypti has been documented in Buenos Aires in the first half of the 20th century (Del Ponte & Blacksley, Reference Del Ponte and Blacksley1947), after the continent-wide control program implemented between 1930 and 1960 this species was considered eradicated from Argentina and its neighboring countries in 1965 (Soper, Reference Soper1967). However, after the cessation of the intensive control measures in the early 1970s, Ae. aegypti reinvaded most of the territory where it was previously present (Eisen & Moore, Reference Eisen and Moore2013), probably transported passively as eggs in used tires (Reiter & Sprenger, Reference Reiter and Sprenger1987).

In Argentina, Ae. aegypti was detected in 1986 in the provinces of Misiones and Formosa (Curto et al., Reference Curto, Boffi, Carbajo, Plastina, Schweigmann and Salomón2002), in 1991 in the locality of Quilmes in the province of Buenos Aires (Campos, Reference Campos1993), and in 1995 in Buenos Aires city (Junín et al., Reference Junín, Grandinetti, Marconi and Carcavallo1995). In the following 3 years, from 1996 to 1998, 810 premises were surveyed in Buenos Aires city, among which 103 (12.7%) were infested, exceeding the minimum thresholds according to the Pan American Health Organization for the transmission of dengue (Schweigmann et al., Reference Schweigmann, Orellano, Kuruk, Vera, Besan, Méndez and Salomón2002). Studies with ovitraps during a 3-year period from 1998 to 2001 showed that this species was established across the city, although with a certain spatial heterogeneity in its distribution, which has been related to urbanization differences (Carbajo et al., Reference Carbajo, Curto and Schweigmann2006), and to differences in temperature dynamics due to the influence of the river (De Majo et al., Reference De Majo, Fischer, Otero and Schweigmann2013).

The seasonal dynamics of Ae. aegypti exhibits a recurrent pattern, with increasing oviposition activity during late spring and early summer (November–January), a peak during late summer (February–March), and a decrease during fall (April–May). No immature stages or adults are observed during winter (Vezzani & Carbajo, Reference Vezzani and Carbajo2008). Thus the population persists through the unfavorable (cold temperature) season in the egg stage, subject to a relatively low mortality under the environmental and climatic winter conditions in Buenos Aires (Fischer et al., Reference Fischer, Alem, De Majo, Campos and Schweigmann2011). The increase in the population abundance during the subsequent spring season starts when the overwintered eggs hatch and larval development initiates as soon as favorable conditions return in spring. The relationship between the seasonal dynamics and temperature has been supported by simulation with mathematical models of population dynamics, in particular for Buenos Aires city (Otero et al., Reference Otero, Schweigmann and Solari2008).

The influence of temperature on Ae. aegypti abundance is especially important at the cool margins of its geographic range, where climate warming is expected to have a critical impact by making these regions more suitable for the mosquito (Eisen & Moore, Reference Eisen and Moore2013). Although Carbajo et al. (Reference Carbajo, Gomez, Curto and Schweigmann2004) found certain variability in total infestation between years for Buenos Aires city, these authors analyzed only a 3-year period, and did not assess the relationship between the inter-annual variability observed and meteorological variables such as temperature or rainfall. Given the short time elapsed from the reinvasion of Ae. aegypti in Buenos Aires city, both the abundance and distribution of this species might still be increasing. Such increases might be a consequence of processes occurring at three different scales: the colonization of new environments (i.e. neighborhoods) via active flight of egg-laying females or passive dispersal of eggs, the colonization of new containers within a neighborhood, and the colonization of the same container by a higher number of individuals. These increases may have important consequences for human health, since this species is the main vector of dengue in America, and increased abundances will have direct impacts on transmission risk (Gubler, Reference Gubler2004).

Long-term abundance data are needed to understand the current status of the invasion of this species near the limits of its distribution range, and to assess the existence and effect of evident changes in climatic variables. Such long-term data are not available within the temperate region.

The aims of this study were to analyze the long-term dynamics of Ae. aegypti in Buenos Aires city over a 16-year study period (1998–2014), to assess the hypothesis of an increase in abundance during recent years, and to relate possible changes in Ae. aegypti abundance with the variation of climatic conditions.

Materials and methods

Study area

Buenos Aires city (34°36S and 58°26′W) is located on the western shore of the Río de La Plata river. The city has a temperate humid climate with seasonally varying temperatures. In fall and spring, the mean temperature varies around 17°C, with cool mornings and nights, whereas in winter the mean temperature is 11.5°C, with moderately cold days and cold nights. In summer, there is strong solar radiation and mean temperatures of 23.6°C. The mean maximum and minimum temperatures are recorded in January and July, respectively. Annual cumulative rainfall is 1200 mm on average, and rainfall events are recorded throughout the year (National Meteorological Service, 2014). The city covers an area of 200 km2, has a population of 2.8 million people, and is surrounded by urban and suburban areas with over 9 million people covering 3600 km2 (Mehrotra et al., Reference Mehrotra, Rosenzweig, Solecki, Natenzon, Omojola, Folorunsho, Gilbride, Rosenzweig, Solecki, Hammer and Mehrotra2011).

Field work

Ae. aegypti oviposition activity was studied with ovitraps throughout nine warm seasons (Austral spring, summer, autumn). The seasons lasted from September to May–June of the following years: 1998/99, 1999/2000, 2000/01, 2007/08, 2009/10, 2010/11, 2011/12, 2012/13 and 2013/14. A total of 200 sites, covering the whole extension of Buenos Aires city (approximately 1 site per km2), were monitored. Ovitraps consisted of a glass flask, painted black on the outside, and filled with tap water up to one third of its volume. One oviposition substrate (wooden paddle) was placed inside each flask, and attached to the wall in a vertical position by a clip (Fay & Eliason, Reference Fay and Eliason1966). Ovitraps were placed on plant beds located on the sidewalks of the roads, at a height less than 1 m, with the criterion of maximizing surrounding vegetation cover. Ovitraps were reconditioned weekly, container walls washed, and water and paddles replaced in situ. This activity was maintained from the beginning of spring through the end of fall, and ended after 2 consecutive weeks without detection of eggs. Missing or broken ovitraps were replaced and classified as inactive during the corresponding week (i.e. not considered in the subsequent analyses). The collected paddles were placed in individual polypropylene bags for transportation, and the eggs on each paddle were counted under a stereoscopic microscope. Ovitraps containing a paddle with eggs were considered positive, while those without eggs were considered negative in the corresponding week. All the eggs collected were assumed to correspond to Ae. aegypti because this is the only container breeding Aedine mosquito species in this region (Rubio et al., Reference Rubio, Bellocq and Vezzani2012).

Data analyses

Temporal dynamics of oviposition activity

The proportion of ovitraps with eggs (number of ovitraps with eggs/total number of active ovitraps) was calculated for each week, and monthly averages of these proportions were calculated. The weekly proportion of traps with eggs was compared among periods with a nonparametric Friedman ANOVA. Post-hoc comparisons were performed with Wilcoxon's test for paired samples, adjusting the significance of the test with the Holm–Bonferroni correction for multiple comparisons (Holm, Reference Holm1979). General indicators were calculated for each season: time of the first detection of eggs, time of the last detection of eggs, duration of oviposition season (number of weeks from the first to the last detection of eggs) and maximum weekly infestation (proportion of ovitraps with eggs in the week with maximum activity).

Site-specific changes in activity

The number of weeks with oviposition activity was calculated for each site and season. From these data, the total number of infested sites (number of sites where activity was detected at least once in the season), and the maximum prevalence at one site (number of weeks with eggs at the site with maximum activity) were calculated. The number of weeks with oviposition activity was compared between consecutive study periods with Wilcoxon's test for paired samples. The significance of the test was adjusted with the Holm–Bonferroni correction for multiple comparisons (Holm, Reference Holm1979).

To assess the long-term changes in activity by site, the mean frequency of activity was calculated for the first three seasons (1998–2001), for the intermediate three seasons (2007–2011), and for the three last seasons (2011–2014). Site specific differences in the average activity period were calculated between 1998–2001 and 2007–2011, and between 2007–2011 and 2011–2014. Then, sites were plotted on maps differentiating areas with average changes larger than 1 week per year (large changes), areas with average changes of 1 week per year or less (small changes), and areas with no changes.

Relationship of oviposition activity with temperature and rainfall

The thermal favorability for each season was analyzed as the number of consecutive gonotrophic cycles (GC) that could potentially be completed. The fraction of the GC that could be completed each hour was calculated based on temperatures and the Sharpe & DeMichele enzyme kinetics approach, using the equation and parameters from Focks et al. (Reference Focks, Haile, Daniels and Mount1993). Hourly temperature data corresponded to the Villa Ortúzar Station, and were provided by the National Meteorological Service of Argentina.

The cumulative GC was calculated for each week from 1 July onwards, and compared among seasons with a nonparametric Friedman ANOVA. Post-hoc comparisons were performed with Wilcoxon's test for paired samples, adjusting the P of the test with the Holm–Bonferroni correction for multiple comparisons (Holm, Reference Holm1979). Two different periods were analyzed: (a) the whole season from 1 July to 30 June, and (b) the beginning of the warm season from 1 July to 31 December, when the main population increase occurs. The relationship of the cumulative number of ovitraps with eggs each period with the corresponding Friedman ANOVA mean rank of GC was assessed with Pearson's correlation analysis.

Weekly cumulative rainfall data were calculated from 1 July to 30 April, and compared with the same method described for GC. The relationship between the cumulative number of positive traps and the following rainfall indicators was assessed with Pearson's correlation analysis: the Friedman ANOVA mean rank of total rainfall, the cumulative rainfall from July to September, the cumulative rainfall from July to December, the cumulative rainfall from July to March, and the number of annual dry events (periods of 15 days or more without rainfall events over 10 mm).

The relationship between monthly oviposition activity and weather variables was analyzed with a General Linear Mixed Model (GLMM) using R software, Version 3.2.3 (R Core Team, 2015), accessed through a user friendly interface in Infostat Software (Di Rienzo et al., Reference Di Rienzo, Casanoves, Balzarini, Gonzalez, Tablada and Robledo2014). Models were fitted using the lme function from the nlme library, and parameters were estimated using the restricted maximum likelihood method (Pinheiro & Bates, Reference Pinheiro and Bates2004). The dependent variable was the monthly average proportions of traps with eggs, and the variables included in the model were: mean temperature of the previous month (T prev), accumulated rainfall during the previous month (R prev), and the interaction of T prev × R prev. Season was included as a categorical variable to specifically assess the hypothesis of increase after accounting for weather variables. Multiple comparisons among seasons were performed with the Di Rienzo, Guzmán and Casanoves test (DGC) with a P value of 0.05 (Di Rienzo et al., Reference Di Rienzo, Guzman and Casanoves2002).

Results

Temporal dynamics of oviposition activity

The first detection of eggs was recorded between the fourth week of September and the second week of November, although in most of the seasons analyzed eggs were first detected between the second and the fourth week of October. Inter-annual variations showed no evident pattern between successive seasons (table 1). The last detection of eggs occurred in mid-May during the first three seasons (1998–2001), and prolonged oviposition activity periods were recorded in the last seasons, at least until the last week of May from 2007 onwards (table 1).

Table 1. General information of Aedes aegypti oviposition activity in nine activity seasons.

A seasonal pattern of oviposition activity was maintained throughout seasons, beginning in early spring (October), and then showing a pronounced increase in late spring and early summer (December–January), maximum activity in late summer (February–March), and a progressive decrease in fall (April–May). The increase in late spring occurred earlier and the decrease in fall later during the last three study periods (white symbols in fig. 1) than during the first three (black symbols in fig. 1).

Fig. 1. Temporal dynamics of Aedes aegypti oviposition activity in nine favorable seasons in Buenos Aires city, Argentina.

A detailed analysis of the pattern during the first months of oviposition activity shows a relatively small initial peak (lasting 1 or 2 weeks), followed by a substantial decrease in activity (at most a few weeks). After this period, a few additional peaks were recorded during the three first seasons (fig. 2a) while in the six remaining seasons a continued increase in abundances was observed (fig. 2b, c). The initial peaks were smallest during the first three study periods, intermediate during the intermediate study periods, and highest during the last three periods.

Fig. 2. Detail of temporal dynamics of Aedes aegypti oviposition activity at the beginning of the favorable season from September to December of: 1998, 1999, 2000 (left); 2007, 2009, 2010 (center); and 2011, 2012, 2013 (right). Arrows indicate the initial peak for each season.

The weekly proportion of ovitraps with eggs showed statistical differences among at least some of the nine periods analyzed (Friedman ANOVA χ2 = 156.7, N = 35, df = 8, P < 0.001), with significant differences between the three early seasons (1998–2001) and the remaining seasons. Although no differences between the subsequent seasons were detected, a general trend towards increased abundances can be observed (fig. 3).

Fig. 3. Weekly proportion of ovitraps with Aedes aegypti eggs (October–May) for different activity seasons in Buenos Aires, Argentina. The same letters indicate seasons with no significant differences.

Site-specific changes in activity

An increase in activity from 1998–2001 to 2011–2014 was recorded throughout the city, and both the total number of infested sites and the maximum prevalence at one site exhibited gradual increases with time during the 16-year study period (table 1). The inter-annual changes within sites showed a significant increase between four inter-annual periods, and a significant decrease only from 2009/10 to 2010/11 (table 2). The previously described pattern of lower activity near the river and higher activity in the periphery was maintained, and in general, sites with null, low and medium activity in 1998–2001 increased to low, medium and high activity respectively in 2011–2014. The 2007–2011 period attained intermediate values. Taking the nine study periods together, all the sites analyzed were positive for Ae. aegypti at least once. The long-term increases in activity occurred throughout the city both from 1998–2001 to 2007–2011 and from 2007–2011 to 2011–2014. Similarly, sites with no changes and sites with reduced activity were interspersed across the city (fig. 4).

Fig. 4. Differences in Aedes aegypti oviposition activity levels between the 3-year averages of 1998–2001 and 2007–2011 (left), and between the 3-year averages of 2007–2011 and 2011–2014 (right). Small and large figures indicate small and large changes, respectively.

Table 2. Inter-annual site-specific changes in oviposition activity.

Note: Bold numbers indicate significant differences.

Relationship of oviposition activity with temperature and rainfall

The cumulative GC showed variations among years, and different patterns were recorded for the whole year and the first half of the oviposition season (fig. 5) The statistical analysis showed significant differences of weekly cumulative GC among seasons both for the whole year period (Friedman ANOVA χ2 = 134.5, N = 52, df = 8, P < 0.001), and for the first half of the oviposition season (Friedman ANOVA χ2 = 149.9, N = 26, df = 8, P < 0.001). Both for the whole year period and for the first half of the season, post-hoc comparisons of mean ranks identified significant differences among some seasons but no clear trend to increasing thermal favorability in later years (fig. 5a, b). The correlation of the cumulative number of ovitraps with eggs each year and the Friedman ANOVA mean rank in the corresponding season was not significant for the whole period (r 2 = 0.20, N = 9, P = 0.22), or for the first half of the season (r 2 = 0.008, N = 9, P = 0.82).

Fig. 5. Cumulative potential gonotrophic cycles (GC) and mean ranks from Friedman ANOVA. (a) Whole year; (b) first half of each oviposition season. The same letters indicate seasons with no significant differences.

The total cumulative rainfall showed variations among years, with the lowest value of 895 mm in 2007/08, the highest value of 1829 mm in 2009/10, and intermediate values in the remaining seasons. Seasons 2007/08, 2010/11 and 2011/12 exhibited cumulative annual and seasonal rainfall amounts and frequency below the averages, whereas seasons 2000/01, 2009/10, 2012/13 and 2013/14 exhibited cumulative annual and seasonal rainfall amounts and frequency higher than the average (table 3).

Table 3. Rainfall statistics and mean ranks from Friedman ANOVA for each of the oviposition seasons studied.

Note: similar letters indicate homogeneous groups. Bold numbers indicate values above the average of the nine study periods.

Significant differences of cumulative weekly rainfall among years were detected (Friedman ANOVA χ2 = 200.8, N = 43, df = 8, P < 0.001). Post-hoc comparison of mean ranks showed differences among most seasons, except for two homogeneous groups (b and e in table 3). The cumulative number of ovitraps with eggs each year was not correlated with the Friedman ANOVA mean rank (r 2 = 0.075, N = 9, P = 0.48), with the cumulative rainfall from July to September (r 2 = 0.061, N = 9, P = 0.52), with the cumulative rainfall from July to December (r 2 = 0.059, N = 9, P = 0.53), with the cumulative rainfall from July to March (r 2 = 0.081, N = 9, P = 0.46), or with the annual number of dry events (r 2 = 0.44, N = 9, P = 0.0502).

GLMM analysis showed a significant and positive relationship of monthly oviposition activity with T prev (P < 0.001), T prev × R prev (P < 0.05), and season (P < 0.05). The obtained model did not include R prev, which showed no significant effect. Post hoc comparisons showed that after adjusting for T prev and T prev × R prev, oviposition activity was significantly higher during the last three seasons than during the remaining seasons, whereas no differences were detected within each group of seasons.

Discussion

This is the first study showing long-term activity pattern for Ae. aegypti in a temperate region, and our results provide detailed information on the short- and long-term dynamics of this species near the southern limit of its distribution. The previously described seasonal pattern for Buenos Aires city has been maintained over successive years, especially the fact that oviposition activity is limited by temperature during the cold months.

The initial peaks in oviposition activity, which seem to be part of a consistent pattern in the Metropolitan Area of Buenos Aires (Campos & Maciá, Reference Campos and Maciá1996; Romeo Aznar et al., Reference Romeo Aznar, Otero, De Majo, Fischer and Solari2013), correspond most likely to the first cohort of adults originated from the hatching of overwintered eggs. Despite certain variability, these initial peaks showed increased importance in consecutive seasons. Furthermore, the magnitude of the subsequent increase in activity, which most likely corresponds to a second cohort of adults originated from eggs laid by the first cohort females, was at least partly related to the magnitude of the initial peak.

The delays in the time of last detection, the consistent increase in the weekly number of ovitraps with eggs, the number of sites with eggs and the frequency of detection per site indicate an increase in the abundance of Ae. aegypti in Buenos Aires city during the 16-year study period, which might be related to an ongoing colonization process. This increase occurs at the neighborhood scale, where the colonization of new environments is reflected by the detection of Ae. aegypti in new sites in consecutive years. These results suggest that there are no absolute barriers for dispersal within Buenos Aires city. At another scale, the colonization of new larval habitats within a neighborhood would be supported by increases in the proportion of water-holding containers that are occupied by immature stages of this species (container index). Although no systematic studies have addressed this issue in Buenos Aires city, independent surveys performed in different years have shown an increase in the container index from 6.4% in 1998 (Schweigmann et al., Reference Schweigmann, Orellano, Kuruk, Vera, Besan, Méndez and Salomón2002) to 13% in 2005 (Schweigmann et al., Reference Schweigmann, Rizzotti, Castiglia, Gribaudo, Marcos, Burroni, Freire, D'Onofrio, Oberlander, Schillaci, Gómez, Maldonado and Serrano2009) and 16.4% in 2011 (Ceriani Nakamurakare et al., Reference Ceriani Nakamurakare, Macchiaverna, Ojeda, Sarracín, Guntín, Contreras, Maqueda, Guillade, Lobato, Pujadas, Cuervo, Rodríguez, Piantanida, Padulles, Anacoreto, Cevey, López Alarcón, Perkins, Álvarez Costa and Burroni2011), suggesting that the use of potential larval habitats has intensified in the last years.

In contrast with mosquito abundances, no consistent inter-annual pattern of change was detected for thermal favorability. Although in Buenos Aires city the temperature is rising at a rate of 0.02°C per year as a consequence of the urban heat island (Barros & Camilloni, Reference Barros and Camilloni1994), the temporal scale of the present study might be insufficient to detect this trend. Regarding water availability, the frequency of dry periods was the only variable related to rainfall that showed a marginally significant change through time, with a reduction in the number of dry periods in recent years. The negative relationship between the magnitude of oviposition activity and the number of dry events suggests that the regular and frequent distribution of rainfall events is favorable for Ae. aegypti population dynamics.

The result of the GLMM analysis shows the variables associated with high oviposition activity. The relationship with temperature has been previously reported for Buenos Aires city (Otero et al., Reference Otero, Schweigmann and Solari2008), and also for other regions along an altitudinal gradient in Mexico (Lozano-Fuentes et al., Reference Lozano-Fuentes, Hayden, Welsh-Rodriguez, Ochoa-Martinez, Tapia-Santos, Kobylinski, Uejio, Zielinski-Gutierrez, Delle Monahe, Monaghan, Steinhoff and Eisen2012) or Nepal (Dhimal et al., Reference Dhimal, Gautam, Joshi, O'Hara, Ahrens and Kuch2015). The fact that months with higher rainfall amounts during the warm period (as suggested by the significant interaction term) attain higher abundances seems also straightforward because of the increased availability of larval habitats after abundant rainfall. Such findings have been reported for the city of Salto, Uruguay, where a significantly higher number of breeding sites have been observed in a rainy year as compared with a year of drought. Moreover, the most productive larval habitats in that study were those filled by rain water (Basso et al., Reference Basso, Garcia da Rosa, Lairihoy, Gonzalez, Roche, Caffera and da Rosa2016). However, the manual filling of containers by the human population is a frequent practice that has been reported in other parts of the world (Morrison et al., Reference Morrison, Gray, Getis, Astete and Sihuincha2004; Kearney et al., Reference Kearney, Porter, Williams, Ritchie and Hoffmann2009; Basso et al., Reference Basso, Garcia da Rosa, Lairihoy, Gonzalez, Roche, Caffera and da Rosa2016), and might provide an alternative source of water in the absence of rainfall. Although no statistics on the filling method of water-holding containers are available for Buenos Aires city, filling independent of rainfall has been inferred from simulations of population dynamics with a mathematical model (Romeo Aznar et al., Reference Romeo Aznar, Otero, De Majo, Fischer and Solari2013). However, the most interesting result is that oviposition activity significantly relates to season after adjusting for weather variables, which suggests that the observed trend of increasing abundances is independent from weather variables.

In short, our results indicate that environmental and climatic conditions in Buenos Aires city have not changed in recent years, but were favorable enough to allow for steady increases in abundance during the last years. This fact, together with the recently documented expansion towards colder areas in Buenos Aires province (Zanotti et al., Reference Zanotti, De Majo, Alem, Schweigmann, Campos and Fischer2015), suggest that local populations of this species might be adapting to lower temperature conditions. Such adaptation has been documented within four generations in a population of Taiwan experimentally exposed to low temperatures in the larval stage (Chang et al., Reference Chang, Hsu, Teng and Ho2007).

Vector abundance, together with a complex array of factors that include the arrival of persons with dengue, play a significant role in the transmission dynamics (Eisen & Moore, Reference Eisen and Moore2013). The transmission dynamics of dengue has been studied with mathematical models for the particular conditions of Buenos Aires city, considering vector abundance dynamics comparable with that shown in our study for 1998–2001 (Otero & Solari, Reference Otero and Solari2010). In this study, the epidemic risk and the size of the final epidemic outbreak have been estimated under different scenarios, and the results suggest that early outbreaks have a very low probability, but are likely (if they occur) to produce large epidemics because of the long time to evolve before the decrease in the vector populations in the cold season. As a consequence of the faster increase in spring abundance of Ae. aegypti during the last years in Buenos Aires, the time window when vector abundances exceed transmission thresholds increases significantly, and conditions become more favorable for early outbreaks with large epidemics. Such conclusions are supported by the increasing importance of local transmission of dengue in Buenos Aires city. The first report of a single case of local transmission in Buenos Aires Metropolitan Area occurred during 2007 (Natiello et al., Reference Natiello, Ritacco, Morales, Deodato, Picollo, Dinerstein and Enria2008). For Buenos Aires city a total of 336 cases of local transmission (1.9 for each imported case) were confirmed 2 years later in 2009 (Ministry of Health of Argentina, 2010), while during the epidemics in 2016 the number of confirmed cases of local transmission increased to 4739, over 9 for each imported case (Ministry of Health of Argentina, 2016). According to our findings, the monitoring of Ae. aegypti should be continued, but also complemented with studies on the abundance, distribution and dynamics of larval habitats in this region.

Acknowledgements

The authors thank María Victoria Gonzalez Eusevi for English review. The Servicio Meteorológico Nacional provided the meteorological data used in this study. The data analyzed in this study were collected by the Grupo de Estudio de Mosquitos (FCEN-UBA) funded by a Convenant endorsed by the Government of the City of Buenos Aires and the Universidad de Buenos Aires. This study was also partially funded by grants PICT 1254-12 (FONCYT) and UBACyT 20020130100778BA (Universidad de Buenos Aires). The authors are grateful to two reviewers whose comments helped to improve the quality of the manuscript.

References

Barros, V. & Camilloni, I. (1994) Urban-biased trends in Buenos Aires’ mean temperature. Climate Research 4, 3345.Google Scholar
Basso, C., Garcia da Rosa, E., Lairihoy, R., Gonzalez, C., Roche, I., Caffera, R.M. & da Rosa, R. (2016) Epidemiologically relevant container types, indices of abundance and risk conditions for Aedes aegypti in Salto (Uruguay), a city under threat of dengue disease. Journal of Emerging Infectious Diseases 103, 19. doi: 10.4172/jeid.1000103.Google Scholar
Becker, N., Pluskota, B., Kaiser, A. & Schaffner, F. (2012) Exotic mosquitoes conquer the world. in Melhorn, H. (Ed.) Arthropods as Vectors of Emerging Diseases. Parasitology Research Monographs 3, 3160. Berlin, Springer.Google Scholar
Campos, R.E. (1993) Presencia de Aedes (Stegomyia) aegypti L. (Diptera: Culicidae) en la localidad de Quilmes (Buenos Aires, Argentina). Revista de la Sociedad Entomológica Argentina 52, 36.Google Scholar
Campos, R.E. & Maciá, A. (1996) Observaciones biológicas de una poblaciones natural de Aedes aegypti (Diptera: Culicidae) en la Provincia de Buenos Aires, Argentina. Revista de la Sociedad Entomológica Argentina 55, 6772.Google Scholar
Carbajo, A.E., Gomez, S.M., Curto, S.I. & Schweigmann, N.J. (2004) Variación espacio-temporal del riesgo de transmisión de dengue en la ciudad de Buenos Aires. Medicina (Buenos Aires) 64, 231234.Google Scholar
Carbajo, A.E., Curto, S.I. & Schweigmann, N.J. (2006) Spatial distribution patterns of oviposition in the mosquito Aedes aegypti in relation to urbanization in Buenos Aires: southern fringe bionomics of an introduced vector. Medical and Veterinary Entomology 20, 209218.Google Scholar
Ceriani Nakamurakare, E., Macchiaverna, N., Ojeda, C., Sarracín, M.P., Guntín, E., Contreras, M., Maqueda, C., Guillade, G., Lobato, P., Pujadas, J., Cuervo, E., Rodríguez, M., Piantanida, P., Padulles, L., Anacoreto, N., Cevey, A., López Alarcón, M., Perkins, A., Álvarez Costa, A. & Burroni, N. (2011) Recipientes criaderos de Aedes aegypti y Culex pipiens en CABA y GBA. 2° Encuentro Nacional sobre Enfermedades Olvidadas y XIV Simposio Internacional sobre Control Epidemiológico de Enfermedades Transmitidas por Vectores, Ciudad de Buenos Aires, Octubre.Google Scholar
Chang, L.H., Hsu, E.L., Teng, H.J. & Ho, C.M. (2007) Differential survival of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) larvae exposed to low temperatures in Taiwan. Journal of Medical Entomology 44, 205210.CrossRefGoogle ScholarPubMed
Curto, S.I., Boffi, R., Carbajo, A.E., Plastina, R. & Schweigmann, N. (2002) Reinfestación del territorio argentino por Aedes aegypti. Distribución geográfica (1994–1999). pp. 127137 in Salomón, O.D. (Ed.) Actualizaciones en Artropodología Sanitaria Argentina. Buenos Aires, Fundación Mundo Sano.Google Scholar
Del Ponte, E. & Blacksley, J.C. (1947) Importancia sanitaria de los Culicidae en la Ciudad de Buenos Aires. Prensa Médica Argentina XXXIV, 821824.Google Scholar
De Majo, M.S., Fischer, S., Otero, M. & Schweigmann, N. (2013) Effects of thermal heterogeneity and egg mortality on differences in the population dynamics of Aedes aegypti (Diptera: Culicidae) over short distances in temperate Argentina. Journal of Medical Entomology 50, 543551.Google Scholar
Dhimal, M., Gautam, I., Joshi, H.D., O'Hara, R.B., Ahrens, B. & Kuch, U. (2015) Risk factors for the presence of Chikungunya and Dengue vectors (Aedes aegypti and Aedes albopictus), their altitudinal distribution and climatic determinants of their abundance in central Nepal. PLoS Neglected Tropical Diseases 9, e0003545.Google Scholar
Di Rienzo, J.A., Guzman, A.W. & Casanoves, F. (2002) A multiple comparisons method based on the distribution of the root node distance of a binary tree obtained by average linkage of the matrix of euclidean distances between treatment means. Journal of Agricultural, Biological and Environmental Statistics 7, 129142.Google Scholar
Di Rienzo, J.A., Casanoves, F., Balzarini, M.G., Gonzalez, L., Tablada, M. & Robledo, C.W. (2014) InfoStat Version 2014. Argentina, Grupo InfoStat, FCA, Universidad Nacional de Córdoba. Available online at http://www.infostat.com.ar.Google Scholar
Eisen, L. & Moore, C.G. (2013) Aedes (Stegomyia) aegypti in the continental United States: a vector at the cool margin of its geographic range. Journal of Medical Entomology 50, 467478.CrossRefGoogle ScholarPubMed
Fay, R.W. & Eliason, D.A.A. (1966) Preferred oviposition sites as surveillance methods for Aedes aegypti . Mosquito News 26, 531535.Google Scholar
Fischer, S., Alem, I.S., De Majo, M.S., Campos, R.E. & Schweigmann, N. (2011) Cold season mortality and hatching behavior of Aedes aegypti L. (Diptera: Culicidae) eggs in Buenos Aires City, Argentina. Journal of Vector Ecology 36, 9499.Google Scholar
Focks, D.A., Haile, D.G., Daniels, E. & Mount, G.A. (1993) Dynamic life table model for Aedes aegypti (Diptera: Culicidae): analysis of the literature and model development. Journal of Medical Entomology 30, 10031017.Google Scholar
Gubler, D.J. (2004) The changing epidemiology of yellow fever and dengue, 1900 to 2003: full circle? Comparative Immunology, Microbiology, and Infectious Diseases 27, 319330.Google Scholar
Holm, S. (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 6570.Google Scholar
Junín, B., Grandinetti, H., Marconi, J.M. & Carcavallo, R.U. (1995) Vigilancia de Aedes aegypti (L) en la ciudad de Buenos Aires (Argentina). Entomología y Vectores 2, 7175.Google Scholar
Kearney, M., Porter, W.P., Williams, C., Ritchie, S. & Hoffmann, A.A. (2009) Integrating biophysical models and evolutionary theory to predict climatic impacts on species’ ranges: the dengue mosquito Aedes aegypti in Australia. Functional Ecology 23, 528538.Google Scholar
Lounibos, P.L. (2002) Invasions by insect vectors of human disease. Annual Review of Entomology 47, 233266.CrossRefGoogle ScholarPubMed
Lozano-Fuentes, S., Hayden, M.H., Welsh-Rodriguez, C., Ochoa-Martinez, C., Tapia-Santos, B., Kobylinski, K.C., Uejio, C.K., Zielinski-Gutierrez, E., Delle Monahe, L., Monaghan, A.J., Steinhoff, D.F. & Eisen, L. (2012) The dengue virus mosquito vector Aedes aegypti at high elevation in Mexico. American Journal of Tropical Medicine and Hygiene 87, 902909.CrossRefGoogle ScholarPubMed
Mehrotra, S., Rosenzweig, C., Solecki, W.D., Natenzon, C.E., Omojola, A., Folorunsho, R. & Gilbride, J. (2011) Cities, disasters and climate risk. pp. 1542 in Rosenzweig, C., Solecki, W.D., Hammer, S.A. & Mehrotra, S. (Eds) Climate Change and Cities: First Assessment Report of the Urban Climate Change Research Network. Cambridge, UK, Cambridge University Press.CrossRefGoogle Scholar
Ministry of Health of Argentina (2010) Situación del dengue en Argentina, primer semestre de 2009. Boletín epidemiológico periódico. Edición especial 2009. Available online at http://www.msal.gob.ar/saladesituacion/epidemiologia_boletines.php.Google Scholar
Ministry of Health of Argentina (2016) Boletín integrado de vigilancia No 312. Semana epidemiológica 22. Available online at http://www.msal.gob.ar/index.php/home/boletin-integrado-de-vigilancia. Bulletin.Google Scholar
Morrison, A.C., Gray, K., Getis, A., Astete, H. & Sihuincha, M. (2004) Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos. Peru. Journal of Medical Entomology 41, 11231142.Google Scholar
Natiello, M., Ritacco, V., Morales, M.A., Deodato, B., Picollo, M., Dinerstein, E., & Enria, D. (2008) Indigenous dengue fever, Buenos Aires, Argentina. Emerging Infectious Diseases 9, 14981499.CrossRefGoogle Scholar
Otero, M. & Solari, H.G. (2010) Stochastic eco-epidemiological model of dengue disease transmission by Aedes aegypti mosquito. Mathematical Biosciences 223, 3246.Google Scholar
Otero, M., Schweigmann, N. & Solari, H.G. (2008) A stochastic spatial dynamical model for Aedes aegypti . Bulletin of Mathematical Biology 70, 12971325.Google Scholar
Pinheiro, J. & Bates, D.M. (2004) Mixed-effects Models in S and S-PLUS. New York, NY, Springer.Google Scholar
R Core Team (2015) R: a Language and Environment for Statistical Computing. Vienna, Austria, R Foundation for Statistical computing. Available online at http://www.R-project.org/.Google Scholar
Reiter, P. & Sprenger, D. (1987) The used tire trade: a mechanism for the worldwide dispersal of container breeding mosquitoes. Journal of the American Mosquito Control Association 3, 494501.Google ScholarPubMed
Romeo Aznar, V., Otero, M., De Majo, M.S., Fischer, S. & Solari, H.G. (2013) Modeling the complex hatching and development of Aedes aegypti in temperate climates. Ecological Modelling 253, 4455.Google Scholar
Rubio, A., Bellocq, M.I. & Vezzani, D. (2012) Community structure of artificial container-breeding flies (Insecta: Diptera) in relation to the urbanization level. Landscape and Urban Planning 105, 288295.Google Scholar
Schweigmann, N., Orellano, P., Kuruk, J., Vera, T.M., Besan, D. & Méndez, A. (2002) Distribución y abundancia de Aedes aegypti (Diptera: Culicidae) en la ciudad de Buenos Aires. pp. 155160 in Salomón, O.D. (Ed.) Actualizaciones en Artropodología Sanitaria Argentina. Buenos Aires, Fundación Mundo Sano.Google Scholar
Schweigmann, N., Rizzotti, A., Castiglia, G., Gribaudo, F., Marcos, E., Burroni, N., Freire, G., D'Onofrio, V., Oberlander, S., Schillaci, H., Gómez, S., Maldonado, S. & Serrano, C. (2009) Información, conocimiento y percepción sobre el riesgo de contraer el dengue en Argentina: Dos experiencias de intervención para generar estrategias locales de control. Cadernos de Saúde Pública 25, 137148.CrossRefGoogle ScholarPubMed
Soper, F.L. (1967) Dynamics of Aedes aegypti distribution and density. Seasonal Fluctuations in the Americas. Bulletin of the World Health Organization 36, 536538.Google Scholar
Vezzani, D. & Carbajo, A.E. (2008). Aedes aegypti, Aedes albopictus, and dengue in Argentina: current knowledge and future directions. Memorias do Instituto Oswaldo Cruz 103, 6674.Google Scholar
Zanotti, G., De Majo, M.S., Alem, I., Schweigmann, N., Campos, R.E. & Fischer, S. (2015) New records of Aedes aegypti at the southern limit of its distribution in Buenos Aires province, Argentina. Journal of Vector Ecology 40, 408411.Google Scholar
Figure 0

Table 1. General information of Aedes aegypti oviposition activity in nine activity seasons.

Figure 1

Fig. 1. Temporal dynamics of Aedes aegypti oviposition activity in nine favorable seasons in Buenos Aires city, Argentina.

Figure 2

Fig. 2. Detail of temporal dynamics of Aedes aegypti oviposition activity at the beginning of the favorable season from September to December of: 1998, 1999, 2000 (left); 2007, 2009, 2010 (center); and 2011, 2012, 2013 (right). Arrows indicate the initial peak for each season.

Figure 3

Fig. 3. Weekly proportion of ovitraps with Aedes aegypti eggs (October–May) for different activity seasons in Buenos Aires, Argentina. The same letters indicate seasons with no significant differences.

Figure 4

Fig. 4. Differences in Aedes aegypti oviposition activity levels between the 3-year averages of 1998–2001 and 2007–2011 (left), and between the 3-year averages of 2007–2011 and 2011–2014 (right). Small and large figures indicate small and large changes, respectively.

Figure 5

Table 2. Inter-annual site-specific changes in oviposition activity.

Figure 6

Fig. 5. Cumulative potential gonotrophic cycles (GC) and mean ranks from Friedman ANOVA. (a) Whole year; (b) first half of each oviposition season. The same letters indicate seasons with no significant differences.

Figure 7

Table 3. Rainfall statistics and mean ranks from Friedman ANOVA for each of the oviposition seasons studied.