Aedes albopictus is an invasive mosquito species that has spread globally and can transmit several arboviruses, including dengue, chikungunya and yellow fever. The species was first reported in Brazil in 1986 and since then has been found in 24 of the 27 Brazilian states, often in peri-urban environments close to highly urbanized areas. To date, population genetics of this important mosquito in areas in the city of São Paulo has not been investigated. In this study, we used 12 microsatellite loci to investigate the microgeographic population genetics of Ae. albopictus, which is present throughout the city of São Paulo. All the analyses revealed structuring of the populations studied, divided into two groups with restricted gene flow between them and without evidence of isolation by distance. We propose two hypotheses to explain the results: (i) low dispersal capability—limited gene flow between populations is due to the low dispersal capability inherent to Ae. albopictus; and (ii) multiple introductions—the structure identified here results from multiple introductions, which led to different dispersal patterns within the city and more genetic heterogeneity. The ability of Ae. albopictus to invade new areas and expand may explain why these mosquito populations appear to be well established and thriving in the city of São Paulo.
Citation: Multini LC, de Souza ALdS, Marrelli MT, Wilke ABB (2019) Population structuring of the invasive mosquito Aedes albopictus (Diptera: Culicidae) on a microgeographic scale. PLoS ONE 14(8): e0220773. https://doi.org/10.1371/journal.pone.0220773
Editor: Abdallah M. Samy, Faculty of Science, Ain Shams University (ASU), EGYPT
Received: May 27, 2019; Accepted: July 23, 2019; Published: August 2, 2019
Copyright: © 2019 Multini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets analyzed during the current study are available in the Mendeley Data repository, DOI: 10.17632/dczghw5p22.1.
Funding: This research was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant 2013/15313-4. LCM is the recipient of a FAPESP fellowship (grant 2015/23386-7). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
There are currently more humans living in urban areas than ever in the history of civilization . As a consequence of this escalating urbanization, significant environmental changes are taking place, resulting, among other things, in increasing temperatures and pollution of the natural environment [2,3].
Inhabiting urban areas is challenging for most species. However, invasive species are more likely to thrive in such environments than native species . Furthermore, urbanization processes often result in a reduction in overall biodiversity followed by an increase in the abundance of the invasive species that have adapted to the urban environment [2,4]. This phenomenon is commonly observed in epidemiologically important mosquito species [4–6].
Mosquito species that have adapted to urban environments can not only be a source of nuisance but also transmit diseases, as in the case of Aedes (Stegomyia) albopictus (Skuse), an aggressive species well adapted to urban environments. This mosquito can breed in artificial containers (e.g., tires and cemetery urns) and blood feed on human hosts, completing its entire life cycle in urban environments [7–9].
Aedes albopictus is native to Asia and has spread to all continents except Antarctica, most likely because of its close relationship with humans [10,11]. As this mosquito undergoes diapause, its eggs can survive for months in artificial breeding containers until the environmental conditions are suitable for hatching and its life cycle can begin [12,13]. This has allowed Ae. albopictus to colonize new areas by being inadvertently dispersed in used tires imported from other countries [14,15]. The species’ adaptability to temperate climates [16–18] and resistance to several insecticides have also favored colonization [19–21].
Aedes albopictus was established in Brazil in 1986 and since then has spread to most of the country, where it is often found in suburban areas with higher vegetation coverage [22,23]. However, a recent entomological survey demonstrated initial evidence of Ae. albopictus domiciliation in a densely populated slum in Rio de Janeiro. The presence of the species in such an environment could indicate its increased adaptation to areas undergoing anthropogenic changes in Brazil .
Aedes albopictus is a competent vector for several arboviruses, including dengue (DENV), chikungunya (CHIKV), Zika (ZIKV) and yellow fever (YFV) [25–30], and was implicated as a primary vector for the CHIKV outbreak in Italy in which 205 cases were notified between July and September 2007 and one death was reported .
Even though the species is not implicated in dengue transmission in Brazil, the DENV-1 serotype has been isolated from its larvae . In addition, ZIKV RNA fragments were found in field-collected eggs of Ae. albopictus in the state of Bahia, Brazil, in an active ZIKV transmission zone . It is also possible that the species is acting as a bridge vector between wild and urban YFV transmission cycles in Brazil, as specimens infected with YFV were recently found in yellow-fever hotspots in two municipalities in the state of Minas Gerais, Brazil [33,34].
Urbanization processes are often responsible for driving the genetic diversity within urban populations of mosquitoes [2,6]. This phenomenon is undoubtedly occurring in mosquito species such as Aedes aegypti, Aedes fluviatilis, Culex nigripalpus, and Culex quinquefasciatus in the city of São Paulo, Brazil [35–38]. São Paulo is a megacity with approximately 12 million people in the center of a metropolitan area with a population of more than 20 million . Thus, identifying the genetic structure of the exotic mosquito Ae. albopictus in São Paulo could lead to a better understanding of how anthropogenic changes to the environment in developing countries such as Brazil are modulating the population structure of this species and the implications of this for vector-borne disease transmission patterns [36,40].
Population genetic studies of Ae. albopictus have been carried out as this species continues to spread worldwide [8,40–43]. A population-structuring study of Ae. albopictus in Manaus, Brazil, found that populations of this species have undergone a recent expansion following a founder effect . Moreover, studies of populations of the species in cities where it has recently become established have shown weak genetic structure and high gene flow, suggesting a dispersal pattern driven mostly by human movements [40,45].
Microsatellite markers are highly polymorphic genetic markers widely used in mosquito population genetics studies [46–48]. Previous studies using these markers provide valuable information on the microgeographic genetic structure of vector mosquitoes in São Paulo, Brazil [35–37]. Considering the high abundance and widespread presence of the invasive species Ae. albopictus in São Paulo , we hypothesize that it is successfully thriving in urban and peri-urban areas. The primary objective of this study was therefore to investigate the microgeographic population genetics of Ae. albopictus throughout the city of São Paulo.
Material and methods
Aedes albopictus samples were collected from 10 urban parks (Anhanguera, Piqueri, Trianon, Burle Marx, Guarapiranga, Ibirapuera, Previdência, Shangrilá, Independência and Nabuco) in different areas of the city of São Paulo (Table 1) [5,49,50]. The parks are relatively small green urban spaces within highly urbanized areas of the city  and were chosen because of the need for collection sites in urbanized areas with high population densities where the traps were unlikely to be tampered with and the researchers would not be exposed to any dangers (Fig 1).
Anhanguera (ANH), Burle Marx (BMX), Piqueri (PQR), Trianon (TRI), Guarapiranga (GRP), Ibirapuera (IBI), Independência (IND), Previdência (PRV), Shangrilá (SHG) and Nabuco (NBC). The map was created with QGIS v2.18.9 (http://www.qgis.org) using layers freely available at http://geosampa.prefeitura.sp.gov.br/PaginasPublicas/_SBC.aspx#. The layers used to the construction of the map were “Administrative limits–District” and “Green Natural Resources–PMMA”.
Mosquito collections were performed monthly from March 2011 to February 2012 and August 2012 to July 2013. Adult mosquitoes were collected with portable, battery-powered aspirators and CDC CO2-baited light traps [51,52]. Mosquitoes were collected over a one-year period in different locations within each urban park. When possible, 30 specimens were selected randomly from each collection site to avoid bias introduced by analyzing siblings (Table 1). The study was approved by the Ethics Committee of the University of São Paulo, and collection permits were provided by the Department of the Environment and Green Areas.
DNA extraction and PCR reactions
DNA was extracted using the DNEasy Blood and Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. Twelve microsatellite primers labeled with a fluorescent dye (FAM, HEX or NED) and 1μL (~1ng) of purified DNA were used in the PCR reactions, which were performed as described before  in an E6331000025 Eppendorf Thermocycler (Mastercycler Nexus Gradient, Eppendorf, Hamburg, Germany) (S1 Table). Amplified fragments were visualized on a 1% agarose gel stained with GelRed Nucleic Acid Gel Stain (Biotium, Hayward, CA, USA) and examined under UV light. PCR products were diluted 1:7 by mixing 3 μL of each product labeled with a different dye with 21 μL of Ultra-Pure Water (Applied Biosystems, Foster City, CA, USA) to a final volume of 30 μL, and 2 μL of the dilution was suspended in 8.925 μL of Hi-Di formamide (Applied Biosystems, Foster City, CA, USA) and 0.075 μL of GeneScan 500 ROX size standard (Applied Biosystems, Foster City, CA, USA) to a final volume of 11 μL. The samples were then sent to the University of São Paulo Center for Human Genome Studies and size-sorted in an ABI 3730 automatic sequencer (Applied Biosystems, Foster City, CA, USA). Fragment analysis was performed using GeneMarker v1.85 (SoftGenetics, State College, PA, USA).
To assess the validity of the microsatellite markers, the number of alleles per locus per population, observed heterozygosity (HO), expected heterozygosity (HE), deviations from Hardy-Weinberg equilibrium (HWE) and inbreeding coefficient (FIS) were calculated with the diveRsity v3.4.0 package in R [53,54]. Linkage disequilibrium was assessed with Genepop v4.2 (http://genepop.curtin.edu.au/) , and HP-Rare 1.0  was used to assess allelic richness and private allelic richness in the populations. Multiple tests were corrected using Bonferroni correction method.
Null-allele frequency was assessed for each locus for each population using FreeNA . In order to test if the null alleles influenced the values of genetic heterogeneity (FST), the same software was used to estimate values of FST unbiased by the presence of null alleles. Mantel test was used to compare both FST values (biased and unbiased) with ade4 package in R [53,58] using 9,999 permutations.
To compute pairwise values of FST (fixation index) and the heterozygosity-based estimator G”ST for measure population structure of all populations, as proposed by Meirmans and Hedrick , the diveRsity package  (v3.4.0) in R was used. To assess the gene flow between populations, a genetic network based on a genotype matrix constructed with FST values was generated using EDENetworks (v2.18) . Analysis of molecular variance (AMOVA) was performed in Arlequin v126.96.36.199  to detect population differentiation. To test for Isolation by distance, a Mantel test between genetic distance (FST/(1-FST)) and geographic distance in kilometers was calculated with ade4 package in R [53,58] using 9,999 permutations.
A Bayesian model-based clustering analysis in STRUCTURE (v2.3.3) was used to infer population structure . Simulations were performed using 100,000 Markov Chain Monte Carlo iterations, with a burn-in period of 100,000 and 10 runs for each value of K (1–10), the analysis was performed assuming Admixture model and correlated allele frequencies between populations. The estimated number of clusters K (ΔK), which identifies genetically homogeneous groups of individuals, was calculated with StructureHarvester (Web v0.6.94) .
Discriminant analysis of principal components (DAPC) was used to further assess genetic structure between populations. DAPC was implemented in R using Adegenet package [53,64] for the whole dataset, where groups membership was defined by K = 2 and K = 3. Only populations that were genetically similar were grouped together, which was confirmed using STRUCTURE and pairwise tests for genetic distance (FST and G”ST) for all populations. A cross-validation analysis was performed in Adegenet using 30 replicates to determine the number of PCs to be retained in the DAPC, the analysis suggested 60 as the number of PCs associated with the lowest root mean squared error.
Hardy-Weinberg equilibrium (HWE) tests conducted for each locus in each population showed HE (expected heterozygosity) greater than HO (observed heterozygosity) in 89% of the conducted tests, indicating deviations from the HWE. Average FIS (inbreeding coefficient) was 0.57072 (S2 Table). After 660 possible comparisons for linkage disequilibrium (LD), 31 tests were statistically significant (P<0.05). However, these results could have been produced by chance, as after Bonferroni correction no loci were linked together across the tested populations.
Allelic richness ranged from 1 (NBC) to 13.34 (BMX), and average private allelic richness was low, varying from 0.12 (SHG) to 1.95 (BMX) (S2 Table). Estimated null-allele frequency in the populations was high (>0.40) for locus Di-6 in one population from ten, Tri-18 in three from ten, Tri-20 in five from ten, Tri-41 and Tri-46 in one from ten. The remaining loci yielded a frequency of 0.30 or lower. Locus Tri-44 had a null-allele frequency of zero (S3 Table). There were no significant differences between FST values biased and unbiased by the presence of null alleles (r = 0.8698, P = 0.001).
Pairwise estimates of the fixation index (FST) and the heterozygosity-based estimator (G”ST) revealed similar results (Table 2). While FST values ranged from 0.0047 to 0.1685, indicating that there is some degree of structuring among the populations, G”ST values were higher (from 0.0525 to 0.446), indicating a high degree of structuring. The lowest FST values were observed between samples collected at GRP, IND, SHG, PRV, and IBI and ranged from 0.0047 to 0.0396, suggesting that these populations are very similar to each other and different from the others. The same was observed for G”ST, which ranged from 0.0525 to 0.1494 for the same populations. Although the values of G”ST were higher than those of FST, the relationship between Ae. albopictus populations were similar for both estimators.
The genetic network separates the populations into three groups (Fig 2), one composed of GRP, IND, SHG, PRV and IBI, in which significant gene flow occurs between all the populations, and another connecting populations IBI and PQR and composed of ANH, BMX, and TRI, which are connected by PQR. The analysis showed population NBC completely segregated from the others.
Each node represents a population. The red node represents the populations with most connections. The node size represents the levels of similarity (nearest routes) between populations. The thickness of the lines represents the estimated coancestry coefficient based on FST values; the larger the coefficient, the thicker the line.
AMOVA showed that 90.80% of the variance was estimated to be within populations (S4 Table). Mantel test showed no correlation between genetic distance (FST/(1-FST)) and geographic distance (S5 Table), indicating no evidence of isolation by distance (IBD) (r = -0.0262; P = 0.5933).
Bayesian cluster analysis
The results of the Bayesian cluster analysis and subsequent application of the Evanno method were used to identify the most likely number of genetic groups, which was two according to the ΔK estimator (S1 Fig). The analysis using K = 2 displayed two well defined genetic clusters, one comprising the populations ANH, BMX, PQR, and TRI (red) and the second, comprising GRP, IBI, IND, PRV, SHG, and NBC (green) (Fig 3A). The subsequent analysis using K = 3 resulted in similar results with high genetic similarity within the groups of populations but with the segregation of NBC from the green cluster into a completely independent new genetic cluster (blue) (Fig 3B).
Discriminant analysis of the principal components (DAPC) and Bayesian analysis using STRUCTURE for all the Aedes albopictus populations, showing the subdivisions for K = 2 (A) and K = 3 (B). Each of the 270 individuals from nine populations is represented by a vertical line divided into different colored segments. The length of each segment represents the probability of the individual belonging to the genetic cluster represented by that color.
Multivariate statistical analysis
DAPC explained 94% of the variance in the data, which was capable of partitioning the genetic variation in K = 2 and K = 3 similarly to STRUCTURE analysis (Fig 3). The relationships between populations identified in STRUCTURE analysis and DAPC were consistent with the results obtained with the genetic distance estimators FST and G”ST, and the genetic network.
Aedes albopictus, a highly anthropophilic species whose dispersal around the world is known to have been mediated by humans , has a very close relationship with humans and has high blood-feeding rates in urban areas. Moreover, urban areas can favor faster larval and pupal development of immature specimens of the species [11,65]. Although some studies have suggested that different levels of urbanization may influence the genetic structure of Ae. albopictus [43,66,67], to date only a handful of studies have addressed this phenomenon at the microscale level . The present study, therefore, investigated the population genetics of Ae. albopictus at the microscale level, considering the distribution of populations of this species in a city.
Our findings suggest all analyses showing the same genetic structuring pattern between the populations: two distinct groups of populations with limited gene flow between them. Both the FST and G”ST estimators revealed the same relationships between the populations, indicating a high level of structuring between the two groups. DAPC and STRUCTURE analysis revealed high genetic diversity between the two population groups and high similarities within the groups. Furthermore, no evidence of IBD was found for the populations, indicating that the source of genetic variation detected could be other than geographic distance.
Our findings also revealed high levels of homozygosis, high values estimated by the inbreeding coefficient, deviations from the HWE and high molecular variability within the Ae. albopictus populations, suggesting that the genetic variation between populations probably occurs at a low hierarchical level. Similar results were previously found for native and invasive Ae. albopictus [8,68], indicating that this is a characteristic of the species and a globally shared pattern . Previous studies have also shown that urbanization and high human population densities may influence the genetic structure of Ae. albopictus populations [43,66].
The deviations from HWE found in this study were consistent with the pressures Ae. albopictus populations are probably undergoing in the city of São Paulo. Processes of population expansion following founder effects are particularly important since they are known for leading to deviations in the HWE. Moreover, although the null alleles probabilities for some loci were considered high (>40%), it did not influence the final results, and removing loci with high probabilities did not enhance the results. The presence of null alleles is common in studies using microsatellite markers and are not believed to invalidate the results [47,69,70]. Similar results were also found for Ae. fluviatilis and Ae. aegypti collected in the same region [35,36]. Moreover, the high values of inbreeding estimated for the populations studied here agree with other studies [8,68,71]. These values may be explained by the restricted gene flow between populations initially founded by a small number of specimens .
Therefore, we believe that there are two hypotheses to explain the pattern of genetic structure in the populations studied here. The first one of the probable causes of the genetic structuring is the limited gene flow between populations due to the low dispersal capability inherent to Ae. albopictus . A study that screened for SNPs (single nucleotide polymorphisms) in Ae. albopictus populations worldwide showed the Brazilian populations to be a monophyletic group. The authors suggested that the samples analyzed were derived from a single Ae. albopictus invasion by a native population from South-East Asia . However, the three Brazilian populations analyzed in the study were from the North and Northeast regions, which are very far from the areas where the populations analyzed here were sampled.
Previous studies have suggested that the dispersal pattern of Ae. albopictus populations may have been chaotic and that this may have played a role in maintaining genetic diversity in invasive populations around the world . Our second hypothesis is therefore that multiple introductions of Ae. albopictus probably occurred. Under this hypothesis, the population structure found in this study is due to multiple introductions of Ae. albopictus in the city of São Paulo, leading to different dispersal patterns within the city and more genetic heterogeneity. Manni et al. , postulated that multiple introductions could increase genetic diversity, favoring expansion and adaptation. The restriction on gene flow between the population groups observed here may also mean that more than one introduction occurred, as the city was already vast in 1986 when this species was first introduced and its topography may have acted as a barrier to gene flow.
There are several mechanisms able to increase or decrease the genetic variation in a given population . Invading and colonizing new areas may have distinct outcomes due to environmental heterogeneity and how local resources can be explored by the invasive species . Moreover, human behavior can actively drive the dispersal of Ae. albopictus, substantially impacting its population dynamics and genetic diversity patterns . At this point, there is no scientific consensus on the outcome of population expansion in the genetic structuring of Ae. albopictus. In our opinion, the two hypotheses suggested here could be explored by future studies by focusing on broader geographic scales and also employing longitudinal experimental designs. This approach would make it possible to identify trends of founder effect and population expansion, and movement and subsequent colonization of new areas by Ae. albopictus.
Previous studies suggested that Ae. albopictus populations have reduced gene flow between densely urbanized areas and rural areas [8,45]. A similar phenomenon was observed in the invasive mosquito Ae. aegypti in the city of São Paulo . However, a positive association between a decrease in genetic structure and an increase in urbanization was found for species native to Brazil, such as Ae. fluviatilis and Cx. nigripalpus [35,38]. This contrast between the genetic structure of native and invasive species may indicate that invasive species are better able to adapt and thrive in urban environments, an advantage that is clearly of epidemiological relevance.
Although the invasive mosquito Ae. aegypti had been eradicated from Brazil, it reinfested the country in the 1970s [75,76]. The species rapidly recolonized the whole country, displaying genetic structuring even on a microgeographic scale [36,77], and is now the primary vector of ZIKV, CHIKV, and DENV in Brazil . The same phenomenon is likely to have occurred with Ae. albopictus as this mosquito was first recorded in Brazil in 1986 and approximately 30 years later had spread to 24 of the 27 Brazilian states .
Aedes albopictus, which is widespread in Brazil, probably entered the country through the port of Espírito Santo and was then passively dispersed in cargo transported on highways, as it was first reported on the Rio-São Paulo highway in 1986 [12,22]. Although the species is found in sylvatic and peri-urban areas in Brazil, its distribution is closely associated with the presence of humans, and it can move quickly between sylvatic and urban environments [7,24]. The ability of Ae. albopictus to invade new areas and expand explains why these mosquito populations appear to be well established and thriving in the city of São Paulo. Human movements throughout the city and the lack of large-scale mosquito control measures in Brazil have probably favored the dispersal and establishment of this species in the city. These factors, together with the epidemiological importance of this mosquito, highlight the need for further studies of the population genetics of this species in Brazil focusing on broader geographic scales and longitudinal experimental designs. Aedes albopictus populations are genetically diverse and can diverge in their ecology and behavior, affecting their resistance to insecticides and vector capacity, among other factors.
S1 Fig. Graph of ΔK showing K = 2 as the most probable number of genetic groups for the Aedes albopictus populations in São Paulo studied here.
S1 Table. Microsatellite loci amplified in Aedes albopictus.
S2 Table. Characterization of the 12 loci analyzed in 10 Aedes albopictus populations.
N (Number of individuals), A (Number of alleles), Ar (Allele richness), Pr (Private allele richness), HO (Observed heterozygosity), HE (Expected heterozygosity), HWE (Hardy-Weinberg Equilibrium) and FIS (Inbreeding Coefficient).
S3 Table. Estimation of null allele frequencies per locus per population of Aedes albopictus from Sao Paulo, Brazil.
S4 Table. Global AMOVA results based on 12 variable loci in the Aedes albopictus populations in São Paulo, Brazil.
The authors would like to thank Walter Ceretti-Junior, Paulo Roberto Urbinatti, and Antônio Ralph Medeiros-Sousa, who kindly helped with field collections, and Aristides Fernandes and Marcia Bicudo de Paula, who identified the specimens.
- 1. United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2014 Revision (2015).
- 2. Johnson MTJ, Munshi-South J. Evolution of life in urban environments. Science. 2017;358: eaam8327. pmid:29097520
- 3. Buhaug H, Urdal H. An urbanization bomb? Population growth and social disorder in cities. Glob Environ Chang. 2013;23: 1–10.
- 4. McKinney ML. Urbanization as a major cause of biotic homogenization. Biol Conserv. 2006;127: 247–260.
- 5. Medeiros-Sousa AR, Fernandes A, Ceretti-Junior W, Wilke ABB, Marrelli MT. Mosquitoes in urban green spaces: using an island biogeographic approach to identify drivers of species richness and composition. Sci Rep. 2017;7: 17826. pmid:29259304
- 6. Knop E. Biotic homogenization of three insect groups due to urbanization. Glob Chang Biol. 2016;22: 228–236. pmid:26367396
- 7. Kamgang B, Wilson-Bahun TA, Irving H, Kusimo MO, Lenga A, Wondji CS. Geographical distribution of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) and genetic diversity of invading population of Ae. albopictus in the Republic of the Congo. Wellcome Open Res. 2018;3: 79. pmid:30175244
- 8. Delatte H, Toty C, Boyer S, Bouetard A, Bastien F, Fontenille D. Evidence of Habitat Structuring Aedes albopictus Populations in Réunion Island. PLoS Negl Trop Dis. 2013;7: e2111. pmid:23556012
- 9. Caputo B, Ienco A, Cianci D, Pombi M, Petrarca V, Baseggio A, et al. The “Auto-Dissemination” Approach: A Novel Concept to Fight Aedes albopictus in Urban Areas. PLoS Negl Trop Dis. 2012;6: 4–11.
- 10. Kraemer MUG, Sinka ME, Duda KA, Mylne A, Shearer FM, Brady OJ, et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci Data. 2015;2: 1–8.
- 11. Kotsakiozi P, Richardson JB, Pichler V, Favia G, Martins AJ, Urbanelli S, et al. Population genomics of the Asian tiger mosquito, Aedes albopictus: insights into the recent worldwide invasion. Ecol Evol. 2017;7: 10143–10157. pmid:29238544
- 12. Consoli RAGB, Lourenço-de-Oliveira R. Principais mosquitos de importância sanitária no Brasil. Cadernos de Saúde Pública. FIOCRUZ: Rio de Janeiro, 1994.
- 13. Forattini OP. Culicidologia Médica, v.2: Identificação, Biologia e Epidemiologia. Culicidologia Médica. EDUSP: São Paulo, 2002.
- 14. Benedict MQ, Levine RS, Hawley WA, Lounibos LP. Spread of The Tiger: Global Risk of Invasion by The Mosquito Aedes albopictus. Vector-Borne Zoonotic Dis. 2007;7: 76–85. pmid:17417960
- 15. Reiter P. Aedes albopictus and The World Trade in Used Tires. 1988–1995: The Shape of Things to Come? Joumal Am Mosq Control Assoc. 1998;14: 83–94.
- 16. Leta S, Beyene TJ, De Clercq EM, Amenu K, Kraemer MUG, Revie CW. Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus. Int J Infect Dis. International Society for Infectious Diseases; 2018;67: 25–35. pmid:29196275
- 17. Hill MP, Axford JK, Hoffmann AA. Predicting the spread of Aedes albopictus in Australia under current and future climates: Multiple approaches and datasets to incorporate potential evolutionary divergence. Austral Ecol. 2014;39: 469–478.
- 18. Roche B, Léger L, L’Ambert G, Lacour G, Foussadier R, Besnard G, et al. The spread of Aedes albopictus in Metropolitan France: Contribution of environmental drivers and human activities and predictions for a near future. PLoS One. 2015;10: 1–13.
- 19. Vontas J, Kioulos E, Pavlidi N, Morou E, della Torre A, Ranson H. Insecticide resistance in the major dengue vectors Aedes albopictus and Aedes aegypti. Pestic Biochem Physiol. Elsevier Inc.; 2012;104: 126–131.
- 20. Pancetti FGM, Honório NA, Urbinatti PR, Lima-Camara TN. Twenty-eight years of Aedes albopictus in Brazil: a rationale to maintain active entomological and epidemiological surveillance. Rev Soc Bras Med Trop. 2015;48: 87–89.
- 21. Aguirre-Obando OA, Martins AJ, Navarro-Silva MA. First report of the Phe1534Cys kdr mutation in natural populations of Aedes albopictus from Brazil. Parasit Vectors. 2017;10: 160. pmid:28347326
- 22. Forattini OP. Identificação de Aedes (Stegomyia) albopictus (Skuse) no Brasil. Rev Saude Publica. 1986;20: 244–245. pmid:3809982
- 23. Camara DCP, Codeço CT, Juliano SA, Lounibos LP, Riback TIS, Pereira GR, et al. Seasonal Differences in Density But Similar Competitive Impact of Aedes albopictus (Skuse) on Aedes aegypti (L.) in Rio de Janeiro, Brazil. PLoS One. 2016;11: e0157120. pmid:27322537
- 24. Ayllón T, Câmara DCP, Morone FC, Gonçalves L da S, Saito Monteiro de Barros F, Brasil P, et al. Dispersion and oviposition of Aedes albopictus in a Brazilian slum: Initial evidence of Asian tiger mosquito domiciliation in urban environments. PLoS One. 2018;13: e0195014. pmid:29684029
- 25. Lambrechts L, Scott TW, Gubler DJ. Consequences of the Expanding Global Distribution of Aedes albopictus for Dengue Virus Transmission. PLoS Negl Trop Dis. 2010;4: e646. pmid:20520794
- 26. Pagès F, Peyrefitte CN, Mve MT, Jarjaval F, Brisse S, Iteman I, et al. Aedes albopictus Mosquito: The Main Vector of the 2007 Chikungunya Outbreak in Gabon. Carter DA, editor. PLoS One. 2009;4: e4691. pmid:19259263
- 27. Smartt CT, Stenn TMS, Chen TY, Teixeira MG, Queiroz EP, Souza Dos Santos L, et al. Evidence of zika virus RNA fragments in Aedes albopictus (Diptera: Culicidae) field-collected eggs from Camaçari, Bahia, Brazil. J Med Entomol. 2017;54: 1085–1087. pmid:28419254
- 28. Chouin-Carneiro T, Vega-Rua A, Vazeille M, Yebakima A, Girod R, Goindin D, et al. Differential Susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika Virus. PLoS Negl Trop Dis. 2016;10: 1–11.
- 29. Amraoui F, Vazeille M, Failloux AB. French Aedes albopictus are able to transmit yellow fever virus. Eurosurveillance. 2016;21: 14–16.
- 30. Wilke ABB, Beier JC, Benelli G. Transgenic Mosquitoes–Fact or Fiction? Trends Parasitol; 2018;34: 1–10.
- 31. Rezza G, Nicoletti L, Angelini R, Romi R, Finarelli A, Panning M, et al. Infection with chikungunya virus in Italy: an outbreak in a temperate region. Lancet. 2007;370: 1840–1846. pmid:18061059
- 32. Serufo JC, Oca HM de, Tavares VA, Souza AM, Rosa R V., Jamal MC, et al. Isolation of dengue virus type 1 from larvae of Aedes albopictus in Campos Altos city, State of Minas Gerais, Brazil. Memórias do Instituto Oswaldo Cruz. 1993. pp. 503–504. pmid:8107613
- 33. World Health Organization–WHO. Yellow Fever–Brazil; 2018. Available: http://www.who.int/csr/don/27-february-2018-yellow-fever-brazil/en/#.WywPs6lNWEc.mendeley
- 34. Oliveira RL, Vazeille M, Filippis AMB, Failloux A-B. Large Genetic Differentiation and Low Variation in Vector Competence for Dengue and Yellow Fever Viruses of Aedes albopictus from Brazil, The United States, And The Cayman Islands. Am J Trop Med Hyg. 2003;69: 105–114. pmid:12932107
- 35. Multini LC, Wilke ABB, Suesdek L, Marrelli MT. Population Genetic Structure of Aedes fluviatilis (Diptera: Culicidae). PLoS One. 2016;11: e0162328. pmid:27598889
- 36. Wilke ABB, Wilk-da-Silva R, Marrelli MT. Microgeographic population structuring of Aedes aegypti (Diptera: Culicidae). PLoS One. 2017;12: e0185150. pmid:28931078
- 37. Wilke ABB, de Carvalho GC, Marrelli MT. Microgeographic Population Structuring of Culex quinquefasciatus (Diptera: Culicidae) From São Paulo, Brazil. J Med Entomol. 2017;54: 1582–1588. pmid:28968880
- 38. Wilke ABB, de Carvalho GC, Marrelli MT. Retention of ancestral polymorphism in Culex nigripalpus (Diptera: Culicidae) from São Paulo, Brazil. Infect Genet Evol. 2018;65: 333–339. pmid:30142383
- 39. IBGE. Instituto Brasileiro de Geografia e Estatística—Estado de São Paulo; 2019 [cited 1 Mar 2019]. Available: https://cidades.ibge.gov.br/brasil/sp/panorama
- 40. Maynard AJ, Ambrose L, Cooper RD, Chow WK, Davis JB, Muzari MO, et al. Tiger on the prowl: Invasion history and spatio-temporal genetic structure of the Asian tiger mosquito Aedes albopictus (Skuse 1894) in the Indo-Pacific. PLoS Negl Trop Dis. 2017;11: e0005546. pmid:28410388
- 41. Beebe NW, Ambrose L, Hill L a., Davis JB, Hapgood G, Cooper RD, et al. Tracing the Tiger: Population Genetics Provides Valuable Insights into the Aedes (Stegomyia) albopictus Invasion of the Australasian Region. PLoS Negl Trop Dis. 2013;7: e2361. pmid:23951380
- 42. Kamgang B, Brengues C, Fontenille D, Njiokou F, Simard F, Paupy C. Genetic structure of the tiger mosquito, Aedes albopictus, in Cameroon (central Africa). PLoS One. 2011;6.
- 43. Goubert C, Minard G, Vieira C, Boulesteix M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity; 2016;117: 125–134. pmid:27273325
- 44. Maia RT, Scarpassa VM, Maciel-Litaiff LH, Tadei WP. Reduced levels of genetic variation in Aedes albopictus (Diptera: Culicidae) from Manaus, Amazonas State, Brazil, based on analysis of the mitochondrial DNA ND5 gene. Genet Mol Res. 2009;8: 998–1007. pmid:19731220
- 45. Schmidt TL, Rašić G, Zhang D, Zheng X, Xi Z, Hoffmann AA. Genome-wide SNPs reveal the drivers of gene flow in an urban population of the Asian Tiger Mosquito, Aedes albopictus. PLoS Negl Trop Dis. 2017;11: 1–20.
- 46. Zielke DE, Werner D, Schaffner F, Kampen H, Fonseca DM. Unexpected Patterns of Admixture in German Populations of Aedes japonicus japonicus (Diptera: Culicidae) Underscore the Importance of Human Intervention. PLoS One. 2014;9: e99093. pmid:24992470
- 47. Gloria-Soria A, Ayala D, Bheecarry A, Calderon-Arguedas O, Chadee DD, Chiappero M, et al. Global genetic diversity of Aedes aegypti. Mol Ecol. 2016;25: 5377–5395. pmid:27671732
- 48. Conn JE, Vineis JH, Bollback JP, Onyabe DY, Wilkerson RC, Póvoa MM. Population structure of the malaria vector Anopheles darlingi in a malaria-endemic region of eastern Amazonian Brazil. Am J Trop Med Hyg. 2006;74: 798–806. pmid:16687683
- 49. Medeiros-Sousa AR, Ceretti W, Urbinatti PR, de Carvalho GC, de Paula MB, Fernandes A, et al. Mosquito fauna in municipal parks of São Paulo City, Brazil: a preliminary survey. J Am Mosq Control Assoc. 2013;29: 275–9. pmid:24199502
- 50. Ceretti-Júnior W, Medeiros-Sousa AR, Bruno Wilke AB, Strobel RC, Dias Orico L, Souza Teixeira R, et al. Mosquito Faunal Survey In a Central Park of the City of São Paulo, Brazil. J Am Mosq Control Assoc. 2015;31: 172–176. pmid:26181694
- 51. Gomes A de C, Rabello EX, Natal D. Uma nova câmara coletora para armadilha CDC-miniatura. Rev Saude Publica. 1985;19: 190–191. pmid:4089514
- 52. Nasci RS. A lightweight battery-powered aspirator for collecting resting mosquitoes in the field. Mosq News. 1981;41: 808–811.
- 53. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2017. pp. 1–12. Available: https://www.r-project.org/
- 54. Keenan K, McGinnity P, Cross TF, Crozier WW, Prodöhl PA. diversity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol. 2013;4: 782–788.
- 55. Rousset F. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol Ecol Resour. 2008;8: 103–106. pmid:21585727
- 56. Kalinowski ST. hp-rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes. 2005;5: 187–189.
- 57. Chapuis M-P, Estoup A. Microsatellite Null Alleles and Estimation of Population Differentiation. Mol Biol Evol. 2006;24: 621–631. pmid:17150975
- 58. Dray S, Dufour A-B. The ade4 Package: Implementing the Duality Diagram for Ecologists. J Stat Softw. 2007;22.
- 59. Meirmans PG, Hedrick PW. Assessing population structure: FST and related measures. Mol Ecol Resour. 2011;11: 5–18. pmid:21429096
- 60. Kivelä M, Arnaud-Haond S, Saramäki J. EDENetworks: A user-friendly software to build and analyze networks in biogeography, ecology and population genetics. Mol Ecol Resour. 2015;15: 117–122. pmid:24902875
- 61. Excoffier L, Laval G, Schneider S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol Bioinform Online. 2005;1: 47–50.
- 62. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155: 945–959. pmid:10835412
- 63. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol. 2005;14: 2611–2620. pmid:15969739
- 64. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24: 1403–1405. pmid:18397895
- 65. Li Y, Kamara F, Zhou G, Puthiyakunnon S, Li C, Liu Y, et al. Urbanization Increases Aedes albopictus Larval Habitats and Accelerates Mosquito Development and Survivorship. PLoS Negl Trop Dis. 2014;8: e3301. pmid:25393814
- 66. Vazeille M, Mousson L, Rakatoarivony I, Villeret R, Rodhain F, Duchemin JB, et al. Population genetic structure and competence as a vector for dengue type 2 virus of Aedes aegypti and Aedes albopictus from Madagascar. Am J Trop Med Hyg. 2001;65: 491–497. pmid:11716103
- 67. Paupy C, Girod R, Salvan M, Rodhain F, Failloux A-B. Population structure of Aedes albopictus from La Réunion Island (Indian Ocean) with respect to susceptibility to a dengue virus. Heredity. 2001;87: 273–283. pmid:11737274
- 68. Sherpa S, Rioux D, Pougnet-Lagarde C, Després L. Genetic diversity and distribution differ between long-established and recently introduced populations in the invasive mosquito Aedes albopictus. Infect Genet Evol. 2018;58: 145–156. pmid:29275191
- 69. Silva Martins WF, Wilding CS, Steen K, Mawejje H, Antão TR, Donnelly MJ. Local selection in the presence of high levels of gene flow: Evidence of heterogeneous insecticide selection pressure across Ugandan Culex quinquefasciatus populations. PLoS Negl Trop Dis. 2017;11: e0005917. pmid:28972985
- 70. Carlsson J. Effects of Microsatellite Null Alleles on Assignment Testing. J Hered. 2008;99: 616–623. pmid:18535000
- 71. Manni M, Gomulski LM, Aketarawong N, Tait G, Scolari F, Somboon P, et al. Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit Vectors. 2015;8: 188. pmid:25890257
- 72. Manni M, Guglielmino CR, Scolari F, Vega-Rúa A, Failloux A-B, Somboon P, et al. Genetic evidence for a worldwide chaotic dispersion pattern of the arbovirus vector, Aedes albopictus. PLoS Negl Trop Dis. 2017;11: e0005332. pmid:28135274
- 73. Rissler LJ. Union of phylogeography and landscape genetics. Proc Natl Acad Sci. 2016;113: 8079–8086. pmid:27432989
- 74. Benedict MQ, Levine RS, Hawley WA, Lounibos LP. Spread of The Tiger: Global Risk of Invasion by The Mosquito Aedes albopictus. Vector-Borne Zoonotic Dis. 2007;7: 76–85. pmid:17417960
- 75. Brathwaite Dick O, San Martin JL, Montoya RH, del Diego J, Zambrano B, Dayan GH. The history of dengue outbreaks in the Americas. Am J Trop Med Hyg. 2012;87: 584–593. pmid:23042846
- 76. Monteiro F, Shama R, Martins AJ, Gloria-Soria A, Brown JE, Powell JR. Genetic Diversity of Brazilian Aedes aegypti: Patterns following an Eradication Program. PLoS Negl Trop Dis. 2014;8: e3167. pmid:25233218
- 77. Louise C, Vidal PO, Suesdek L. Microevolution of Aedes aegypti. PLoS One. 2015;10: e0137851. pmid:26360876
- 78. Lourenço AF, Rodrigues FM. Diseases Transmitted by the Aedes aegypti (Linnaeus, 1762) in Brazil in the Last Ten Years. Estudos. 2017;44: 72.