# Genetic evidence for a worldwide chaotic dispersion pattern of the arbovirus vector, Aedes albopictus.

Mosè Manni, Carmela R Guglielmino, Francesca Scolari, Anubis Vega-Rúa, Anna-Bella Failloux, Pradya Somboon, Antonella Lisa, Grazia Savini, Mariangela Bonizzoni, Ludvik M Gomulski, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Mosè Manni, Carmela R Guglielmino, Francesca Scolari, Anubis Vega-Rúa, Anna-Bella Failloux, et al. Genetic evidence for a worldwide chaotic dispersion pattern of the arbovirus vector, Aedes albopictus. PLoS Neglected Tropical Diseases, 2017, 11 (1), pp.e0005332. 10.1371/journal.pntd.0005332. pasteur-01468191

HAL Id: pasteur-01468191

https://pasteur.hal.science/pasteur-01468191

Submitted on 15 Feb 2017

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

RESEARCH ARTICLE

# Genetic evidence for a worldwide chaotic dispersion pattern of the arbovirus vector, *Aedes albopictus*

Mosè Manni¹, Carmela R. Guglielmino¹, Francesca Scolari¹, Anubis Vega-Rúa²,³, Anna-Bella Failloux³, Pradya Somboon⁴, Antonella Lisa⁵, Grazia Savini¹, Mariangela Bonizzoni¹, Ludvik M. Gomulski¹, Anna R. Malacrida¹, Giuliano Gasperi¹*

1 Department of Biology and Biotechnology, University of Pavia, Pavia, Italy, 2 Laboratory of Medical Entomology, Environment and Health Unit, Morne Jolivière, Institut Pasteur of Guadeloupe, Les Abymes, Guadeloupe (French West Indies), 3 Department of Virology, Arboviruses and Insect Vectors Unit, Institut Pasteur, Paris, France, 4 Department of Parasitology, Chiang Mai University, Chiang Mai, Thailand, 5 Computational Biology Unit, Institute of Molecular Genetics-National Research Council, Pavia, Italy

* gasperi@unipv.it

## Abstract

<figure><img src="image_5.png" /></figure>

OPEN ACCESS

**Citation:** Manni M, Guglielmino CR, Scolari F, Vega-Rúa A, Failloux A-B, Somboon P, et al. (2017) Genetic evidence for a worldwide chaotic dispersion pattern of the arbovirus vector, *Aedes albopictus*. PLoS Negl Trop Dis 11(1): e0005332. doi:10.1371/journal.pntd.0005332

**Editor:** Charles Apperson, North Carolina State University, UNITED STATES

**Received:** September 14, 2016

**Accepted:** January 16, 2017

**Published:** January 30, 2017

**Copyright:** © 2017 Manni 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 Statement:** The raw genotype data has been submitted to the Open Science Framework data repository (DOI 10.17605/OSF.IO/U4JRP). All other relevant data are within the paper and its Supporting Information files.

**Funding:** This study received support from the EU-FP7 Research Infrastructures project (INFRAVEC) (http://infravec.eu), Grant N° 228421 (GG) and also from the Italian Ministry of Health (http://www.salute.gov.it), Grant N° RF-2010-2318965 (ARM), the Fondazione Banca del Monte di Lombardia,

## Background

Invasive species represent a global concern for their rapid spread and the possibility of infectious disease transmission. This is the case of the global invader *Aedes albopictus*, the Asian tiger mosquito. This species is a vector of medically important arboviruses, notably chikungunya (CHIKV), dengue (DENV) and Zika (ZIKV). The reconstruction of the complex colonization pattern of this mosquito has great potential for mitigating its spread and, consequently, disease risks.

## Methodology/Principal findings

Classical population genetics analyses and Approximate Bayesian Computation (ABC) approaches were combined to disentangle the demographic history of *Aedes albopictus* populations from representative countries in the Southeast Asian native range and in the recent and more recently colonized areas. In Southeast Asia, the low differentiation and the high co-ancestry values identified among China, Thailand and Japan indicate that, in the native range, these populations maintain high genetic connectivity, revealing their ancestral common origin. China appears to be the oldest population. Outside Southeast Asia, the invasion process in La Réunion, America and the Mediterranean Basin is primarily supported by a chaotic propagule distribution, which cooperates in maintaining a relatively high genetic diversity within the adventive populations.

## Conclusions/Significance

From our data, it appears that independent and also trans-continental introductions of *Ae. albopictus* may have facilitated the rapid establishment of adventive populations through admixture of unrelated genomes. As a consequence, a great amount of intra-population
Pavia, Italy (http://www.fbml.it)(GG), and Cariplo-Regione Lombardia “IMPROVE” (http://www.fondazionecariplo.it)(FS). 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.

variability has been detected, and it is likely that this variability may extend to the genetic mechanisms controlling vector competence. Thus, in the context of the invasion process of this mosquito, it is possible that both population ancestry and admixture contribute to create the conditions for the efficient transmission of arboviruses and for outbreak establishment.

## Author Summary

*Aedes albopictus*, the Asian tiger mosquito is the most invasive species in the world. Over the past 30 years, from its native tropical Southeast Asia it rapidly spread across the world, and it is currently present in several tropical, subtropical and temperate countries due to its high ecological plasticity. Its dramatic global expansion has resulted in public health concerns as a consequence of its vector competence for several arboviruses, such as chikungunya, dengue and Zika, which are the most prevalent pathogens of humans. The identification of the migration routes and, consequently, the demographic origin of mosquito adventive populations is a priority to prevent local outbreaks of viral infection. Using a combined population genetics and Bayesian approach, we found that the worldwide invasion of this mosquito has not occurred through a step-by-step expansion process from the native Southeast Asia. A recent and chaotic human-mediated propagule distribution allowed adventive populations to be established in the Indian Ocean, Mediterranean Basin, and North America regions. We have detected high intra-population variability, which may co-operate with the genetic mechanisms controlling vector competence.

## Introduction

The term ‘successful biological invasion’ includes the detrimental impact on human health, economy and native biodiversity [1–3]. Indeed, invasive species represent a global concern especially because of their rapid spread and competitive nature, and the possibility of transmission of infectious diseases. The ability of a species to establish and become invasive can be influenced by a variety of factors including its life history traits, ecological and environmental constraints, and the intensity of propagule pressure [4, 5]. The on-going intensification of the global transportation networks offers new opportunities for species to disperse, even into remote regions [6, 7]. A new term, Anthropocene, has been suggested [8] to name the present epoch characterized by human impacts on biota. By creating new bioconnectivity among ecosystems, human activity is creating new ecological niches of adaptation for invasive species and this represents an increasingly important factor in contemporary evolution [9].

*Aedes* mosquito species [10] offer a variety of case histories of successful invasions, mainly human mediated [11], and provide examples of how human activities have impacted their evolutionary histories [7, 12, 13]. This is the case for important arbovirus vector species such as *Ae. aegypti*, *Ae. albopictus* and *Ae. japonicus japonicus* [14]. Their eggs, in favourable conditions of humidity and temperature, can survive long transportation times so that a large number of viable eggs can be accidentally transported [15–17]. On the other hand, anthropogenic activities also create new breeding and trophic niches of adaptation in close proximity to human living sites, impacting their relationships with humans [12, 13]. The global invasion of *Ae. albopictus* (Skuse, 1894), the Asian tiger mosquito, is an example of human-aided transport [18] with important public health implications. This mosquito is listed among the top hundred most dangerous invasive species [19]. Originally, it was a zoophilic forest species which, from tropical East Asia [20,
21], spread first to the Indian and Pacific Ocean islands [22] and, during the 1980s, rapidly expanded its range, also across temperate regions, in Europe, the Americas and Africa [14, 23-26]. Ecological, demographic, behavioural genetic/genomic studies indicated that this mosquito is able to tolerate climate/environment interactions that differ from its native range [27-29]. One of the most important adaptive life history traits is its ability to diapause in the presence of unfavorable conditions [30-32]. The rapid global spread of *Ae. albopictus* is creating public concern due to its vector competence for at least 20 arboviruses, notably chikungunya (CHIKV), dengue (DENV) and Zika (ZIKV) viruses [16, 33-42]. Indeed, outside its original range this mosquito has been increasingly involved in local autochthonous transmission of chikungunya and dengue in many places where it has become established, including La Réunion, continental Europe, Africa, the Americas and Japan [43, 44]. In Europe, the first chikungunya outbreak took place in 2007 in Italy in the Ravenna/Cesena area, with more than 200 confirmed cases [45, 46]. Later outbreaks occurred in France [47] and Croatia [48]. Therefore, the identification of important dispersal routes has great potential for mitigating the spread of the mosquito and for preventing/reducing the risk of outbreaks [49]. Plausible routes of introduction of *Ae. albopictus* at the global scale have been inferred from historical and observational data [50]. These data, however, are not informative for the demographic histories of the adventive populations in the different countries. Population genetic approaches using mtDNA, rDNA and microsatellite markers are providing, at different spatial scales, data on the phylogeographical relationships among populations and on their genetic diversity [51-58]. However, these data are highly disparate for providing a synthesis of the demographic history of this mosquito [59].

Previously, we suggested that the global expansion of *Ae. albopictus* was primarily supported by a chaotic propagule distribution mediated by human activity [60]. In order to support this hypothesis, here we combine classical population genetics analyses and Approximate Bayesian Computation (ABC) approaches to infer the demographic history of populations from representative countries in the Southeast Asian native range and in the more recently colonized areas.

Specifically, we provide information on the origins of populations from Southeast Asia (i.e., China, Thailand, Japan), the Indian Ocean, the Mediterranean Basin, the Pacific Ocean islands and North America. Since tiger mosquito populations display differences in vectorial capacity for different arboviruses [35, 38, 61-63], the outcomes from this paper are valuable to develop models that may predict the risk of vector outbreaks [59].

# Methods

## Mosquito collections

During a period from 2010 to 2012, tiger mosquito samples were collected as eggs from public land in ten different localities: three from the native Asian region (China, Japan, Thailand) and seven from invaded areas: Indian Ocean (La Réunion Island), Mediterranean Basin (Italy, Albania, Greece), Pacific Ocean (Hawaii) and North America (U.S.A.) (Table 1). These localities were chosen as sampling sites because data on the history and ecology of their well-established populations are available. To avoid any seasonality effect on population size and dynamics, especially in temperate regions, eggs were collected in the peak of population size (July-August), i.e., not in the diapause or pre-diapause period [32]. A standardized sampling protocol was adopted in order to minimize the inbreeding effect [59, 64]. In each locality, 15 to 17 ovitraps were placed at a distance of least 500 m one to another, and at least 40 eggs/ovitrap were obtained. The taxonomic identification of the emerging adults was confirmed using morphological keys [65]. Three individuals emerging from each ovitrap collection were pooled for each locality, and part was supplied to our
<table><caption>**Table 1.** *Aedes albopictus* populations sampled in areas in which the presence of this mosquito has been historically recognised. Dates refer to the first observational record in the country of the sampling sites.</caption><thead><tr><th>Area</th><th>Country</th><th>First historical record</th><th>Sample site</th><th>Sample name</th><th>Sample size</th><th>Latitude</th><th>Longitude</th><th>Date of collection</th></tr></thead><tbody><tr><td rowspan="3">East Asia</td><td>Japan</td><td>-</td><td>Nagasaki</td><td>JP</td><td>13</td><td>32°75′N</td><td>129°88′E</td><td>2011</td></tr><tr><td>China</td><td>-</td><td>Xiamen</td><td>CN</td><td>10</td><td>24°47′N</td><td>118°09′E</td><td>2011</td></tr><tr><td>Thailand</td><td>-</td><td>Ban Rai</td><td>TH</td><td>30</td><td>15°30′N</td><td>99°45′E</td><td>2010</td></tr><tr><td>Indian Ocean</td><td>Réunion</td><td>18<sup>th</sup> Century [37]</td><td>Saint Pierre</td><td>RE</td><td>30</td><td>21°32′S</td><td>55°47′E</td><td>2010</td></tr><tr><td>Mediterranean</td><td>Greece</td><td>2003 [66, 67]</td><td>Athens</td><td>GR</td><td>29</td><td>37°98′N</td><td>23°73′E</td><td>2011</td></tr><tr><td rowspan="3">basin</td><td>Albania</td><td>1979 [37]</td><td>Tirana</td><td>AL</td><td>24</td><td>41°33′N</td><td>19°83′E</td><td>2011</td></tr><tr><td rowspan="2">Italy</td><td>1990 [37]</td><td>Cesena</td><td>IT1</td><td>31</td><td>44°14′N</td><td>12°25′E</td><td>2010</td></tr><tr><td></td><td>Brescia</td><td>IT2</td><td>26</td><td>45°54′N</td><td>10°22′E</td><td>2010</td></tr><tr><td>Pacific Ocean</td><td>Hawaii</td><td>~1900 [68]</td><td>Oahu</td><td>HI</td><td>29</td><td>21°43′N</td><td>158°00′W</td><td>2012</td></tr><tr><td>North America</td><td>U.S.A.</td><td>1985 [69]</td><td>Manassas (Virginia)</td><td>VA</td><td>30</td><td>38°75′N</td><td>77°47′W</td><td>2012</td></tr></tbody></table>

doi:10.1371/journal.pntd.0005332.t001

laboratory as ethanol-preserved specimens, from which total genomic DNA was individually extracted.

## Microsatellite genotyping

Each mosquito was screened for seventeen previously characterized microsatellite loci [60], i. e., from Aalbmic1 to Aalbmic17. These loci, which are spread across the genome, ensure sufficient power to detect genetic variability even in relatively small samples [70]. PCR reactions and fragment resolution were performed as we previously described [60]. The TANDEM program [71] for the automated binning of allele lengths (with additional manual checking) was used to overcome the problems of genotyping errors. In cases in which microsatellite amplification was not successful or scoring was uncertain, re-extraction of DNA was performed.

## Data analyses

Genetic variation within each locality was estimated in terms of average numbers of alleles ($n_a$), number of private alleles ($n_p$) and frequency of private alleles ($A_p$) using GenAlEx 6.5 [72, 73]. The average number of alleles and private alleles were also computed at the individual level: $n_a/n$ and $n_p/n$, respectively. The inbreeding index ($F_{IS}$) was obtained using FSTAT V.2.9.3.2 [74]. Observed and expected heterozygosity and Pairwise-$F_{ST}$ [75] values were computed using Microsatellite Analyser V.4.05 [76]. The statistical significance of each $F_{ST}$ value was assessed by comparison of the observed value with the values obtained in 10,000 matrix permutations and Bonferroni corrections were applied. Linkage disequilibrium between pairs of loci in each sample (100 batches, 1000 iterations per batch) and deviations from Hardy-Weinberg equilibrium (HWE) at each locus/sample combination were examined with GENE-POP V. 4.2 [77, 78] and the statistical significance was assessed following Bonferroni corrections [79]. The allelic polymorphic information content and null allele frequencies ($A_n$) for each locus were estimated using CERVUS [80].

## Population structure and demographic inference

The relationships among populations were assessed using Principal Coordinate Analysis in GenAlEx V.6.4 [73]. Bayesian clustering analysis in STRUCTURE V 2.3.2 [81–83] was used to infer population structure using the admixture model and assuming independent allele frequencies. The burn-in was set to 500,000 steps and was followed by 1,000,000 Markov Chain Monte Carlo replications. All runs were repeated 20 times for each number of possible clusters
(K), set between 1 and 10 (i.e., the number of samples). The appropriate number of genetic clusters was determined by plotting the log probability (L(K)) and ΔK across multiple runs [84] as implemented in STRUCTURE HARVESTER [85]. Finally, the programs CLUMPP [86] and DISTRUCT [87] were used to average replicate runs and to generate bar graphs of structure results, respectively.

## Definition of invasion scenarios

To infer the invasion routes followed by *Ae. albopictus*, competing hypotheses regarding population divergence at the global scale were compared using an Approximate Bayesian Computation (ABC) method, as implemented in DIYABC v2.0 [88]. Because of the huge number of possible scenarios (10!) that could be tested with the ten considered populations, a step by step approach has been adopted considering subgroups of populations, starting from the putative ancestral area to new invaded regions. Southeast Asia was assumed as the ancestral region considering that it has been suggested to be the species home range. On this basis, plausible invasion scenarios were set considering the historical knowledge of the first record in invaded countries and the data obtained from population genetic analyses (i.e., population structure and ancestry). The competing scenarios were set using prior definitions and distribution of demographic parameters, as described in S1 Table. We took into account the effective population size, the timing in which split or admixture events occurred, the number of founders which contributed to the establishment of adventive populations, the duration of the eventual bottlenecks occuring during colonization and the rate of admixture, if considered. The prior parameters were kept deliberately broad when no prior information were available. This is the case for the effective population sizes, for which broad priors [500–100,000] with uniform distributions were chosen for all the populations. The time range of each event was kept less broad when it was supported by historical data. The timing of events was expressed in numbers of generations back in time. Since the number of generations/year is dependent on bioclimatic conditions, we assumed around 4–7 generations/year in temperate regions, and 12–17 generations in tropical regions.

Although *Ae. albopictus* has great potential for rapid population growth [89], uncertainty regarding the duration of the bottleneck was taken into account. Therefore, we assumed a bottleneck period with a uniform prior distribution bounded between 0 and 50 generations. During colonization, the number of founder individuals for each colonization event was described as *NF* and its size was drawn from a uniform distribution bounded between 1 and 100 individuals. If necessary, the rate of admixture between two populations was drawn in a uniform distribution and set from 0.001 to 0.999. For the genetic parameters, the microsatellite Generalized Stepwise Mutation model was considered, and a mean microsatellite mutation rate across loci was set between $10^{-5}$ and $10^{-3}$. The parameter of geometric distribution was specified with a uniform prior distribution bounded between 0.1–0.3. For each locus the above considered mutation parameters were allowed to vary using Gamma distributions. Finally, the possibility of single nucleotide insertion/deletion mutations were considered with a mean frequency of $10^{-8}-10^{-5}$ (S1 Table).

The genetic variation within and among the populations was summarized using sets of statistics conventionally used in ABC analyses: the mean number of alleles per locus, the mean expected heterozygosity, the mean allelic size variance, the overall pairwise $F_{ST}$ values, and the Garza-Williamson index (the number of alleles in a population divided by the range in allele size)[90]. This index helps to discriminate populations which have experienced recent losses in genetic variability from those that have been stable for a long time. One million simulated datasets were generated for each scenario. Competing scenarios were compared by calculating
their posterior probabilities using a polychotomous logistic regression on the 1% of simulated data closest to the observed data [91]. Confidence in scenario choice was evaluated by computing type I and type II errors [92]. Once the most likely scenario was identified for each analysis, the posterior distributions of genetic and demographic parameters were estimated. This was achieved by computing a local linear regression on the 1% of the simulated data closest to our observed dataset, after applying a logit transformation to the parameters value. The goodness-of-fit of the estimation procedure was also evaluated by performing a model checking computation by generating 1000 pseudo-observed data sets with parameter values drawn from the posterior distribution given the most likely scenario.

# Results

## Intra-specific variation

The considered seventeen microsatellite loci, scored in 252 mosquitoes across the 10 localities from Asia to America, displayed different levels of polymorphism in terms of number of alleles, ranging from 2 alleles (Aealbmic17) to 24 alleles (Aealbmic5). The number of alleles per locus in each population is provided in S2 Table and the original genotype data are available in Open Science Framework data repository (doi: 10.17605/OSF.IO/U4JRP). Overall these loci display a mean Polymorphic Information Content of 0.54 suggesting that they are sufficiently informative for assessing the degree of population variability and structuring of this mosquito. Results of Fisher's exact tests, after sequential Bonferroni correction [79], revealed significant departures from HWE in 13 out of 170 population/locus combinations. However, the HWE departures were not concentrated at any locus or in any population. No consistent pattern of linkage disequilibrium between any particular pair of loci was observed, therefore the 17 loci were considered as independent and their variability might well reflect genome-wide patterns across populations.

Variability at these loci appeared relatively high across the ten populations (Table 2) but in the Asian region, the China (CN), Japan (JP), and Thailand (TH) populations displayed a relatively higher level of variation and this was particularly evident when the number of alleles is referred to the number of individuals (n<sub>a</sub>/n). While private alleles were detected in all populations, the highest proportion was estimated in the Asian region and in La Réunion island. An estimate of variability distribution in and among the populations (AMOVA) indicated that

<table><caption>**Table 2.** Genetic variability estimates of ten *Ae. albopictus* samples from different geographical regions. n<sub>a</sub>, mean number of alleles; n<sub>a/n</sub> mean number of alleles/individual; n<sub>p</sub>, number of private alleles; n<sub>p/n</sub> mean number of private alleles/individual; A<sub>p</sub>, mean frequency of private alleles; H<sub>O</sub>, mean observed heterozygosity; H<sub>E</sub>, mean expected heterozygosity; A<sub>n</sub>, mean frequency of null alleles.</caption><thead><tr><th>Populations</th><th>n<sub>a</sub></th><th>n<sub>a/n</sub></th><th>n<sub>p</sub></th><th>n<sub>p/n</sub></th><th>A<sub>p</sub></th><th>H<sub>O</sub></th><th>H<sub>E</sub></th><th>A<sub>n</sub></th></tr></thead><tbody><tr><td>JP</td><td>4.71</td><td>0.36</td><td>5</td><td>0.38</td><td>0.06</td><td>0.41</td><td>0.55</td><td>0.09</td></tr><tr><td>CN</td><td>3.71</td><td>0.37</td><td>4</td><td>0.40</td><td>0.09</td><td>0.41</td><td>0.56</td><td>0.10</td></tr><tr><td>TH</td><td>6.06</td><td>0.20</td><td>8</td><td>0.27</td><td>0.03</td><td>0.44</td><td>0.57</td><td>0.08</td></tr><tr><td>RE</td><td>5.65</td><td>0.19</td><td>13</td><td>0.43</td><td>0.04</td><td>0.53</td><td>0.58</td><td>0.03</td></tr><tr><td>GR</td><td>4.29</td><td>0.15</td><td>4</td><td>0.14</td><td>0.02</td><td>0.42</td><td>0.50</td><td>0.05</td></tr><tr><td>AL</td><td>4.18</td><td>0.17</td><td>5</td><td>0.21</td><td>0.04</td><td>0.38</td><td>0.48</td><td>0.07</td></tr><tr><td>IT1</td><td>4.94</td><td>0.16</td><td>2</td><td>0.06</td><td>0.02</td><td>0.47</td><td>0.51</td><td>0.03</td></tr><tr><td>IT2</td><td>3.65</td><td>0.14</td><td>4</td><td>0.15</td><td>0.04</td><td>0.42</td><td>0.51</td><td>0.06</td></tr><tr><td>HI</td><td>3.65</td><td>0.12</td><td>1</td><td>0.03</td><td>0.02</td><td>0.34</td><td>0.45</td><td>0.08</td></tr><tr><td>VA</td><td>4.88</td><td>0.16</td><td>4</td><td>0.13</td><td>0.03</td><td>0.45</td><td>0.52</td><td>0.05</td></tr></tbody></table>

JP, Japan; CN, China; TH, Thailand; RE, La Réunion; GR, Greece; AL, Albania; IT1, Italy1/Cesena; IT2, Italy2/Brescia; HI, Hawaii; VA, Virginia (U.S.A.).

doi:10.1371/journal.pntd.0005332.t002
most variation (85%) occurred within individuals, whereas only about 15% of total variation was detected among populations.

## Population structure

Pairwise FST values were significantly different from zero in all the comparisons (Table 3). The lowest estimates were detected among the Asian populations (0.028 between CN-JP, 0.051 between TH-JP). By contrast, within the Mediterranean area, there was heterogeneity in terms of pairwise-FST values. Two geographically close regions such as Greece and Albania were the most differentiated (FST = 0.187), but Greece displayed a certain genetic affinity with Thailand (FST = 0.083), and Albania with China (FST = 0.075). In America, Virginia shared greatest genetic affinity with the Asian populations (especially with Japan) but the lowest FST value was with the IT2 (Brescia) Italian population (FST = 0.057). La Réunion, besides being linked to the Asian region, was genetically related to the Mediterranean area, with the exclusion of Greece (FST = 0.148). The overall absence of correlation between genetic differentiation (FST) and geographical distance was confirmed by the R² value = 0.009, P = 0.649 (S1 Fig). These population relationships were evident from the Principal Coordinate Analysis plot (S2 Fig). Also the Bayesian method implemented in STRUCTURE showed that the coancestry proportion among individuals of the ten populations was not consistent with their geographic proximity. The natural logarithm of the likelihood of the data, ln(P(X/K)), increased from K = 1 to K = 4 and then gradually reached a plateau towards 10, suggesting that the genetic clustering of the ten populations was 4. The individual mosquitoes from the ten populations were then assigned to each of four clusters with a certain probability value (Fig 1; S3 Table). The individuals of China were mainly represented in cluster 2 (red) together with Japan and partially with Thailand. Cluster 2 could be therefore identified as the Asian cluster. The co-ancestry of the Mediterranean populations was heterogeneous: Albania was tightly related (57%) to the Asian cluster 2, while Greece was mainly represented in cluster 1 (blue)(83%) related only to Thailand (34%). Also the ancestry of the two North Italian populations (IT1, IT2) was heterogeneous. IT2 (Brescia) shared about 80% of coancestry in cluster 4 (yellow), with the Hawaiian and North American (Virginia) samples. By contrast, the ancestry of IT1 (Cesena) was fragmented between Asian cluster 2 (38%), American cluster 4 (31%), but also with La Réunion cluster 3 (green)(29%).

<table><caption>Table 3. Matrix of geographic distance (km) (above diagonal) and pairwise-FST values (below diagonal) among wild *Ae. albopictus* samples.</caption><thead><tr><th></th><th>JP</th><th>CN</th><th>TH</th><th>RE</th><th>GR</th><th>AL</th><th>IT1</th><th>IT2</th><th>HI</th><th>VA</th></tr></thead><tbody><tr><th>JP</th><td></td><td>1,473</td><td>3,631</td><td>9,931</td><td>9,068</td><td>9,117</td><td>9,400</td><td>9,414</td><td>7,127</td><td>11,682</td></tr><tr><th>CN</th><td>0.028*</td><td></td><td>2,199</td><td>8,478</td><td>8,731</td><td>8,894</td><td>9,314</td><td>9,379</td><td>8,464</td><td>12,925</td></tr><tr><th>TH</th><td>0.051</td><td>0.047</td><td></td><td>6,301</td><td>7,739</td><td>8,044</td><td>8,621</td><td>8,752</td><td>10,648</td><td>14,164</td></tr><tr><th>RE</th><td>0.079</td><td>0.073</td><td>0.068</td><td></td><td>7,391</td><td>7,886</td><td>8,514</td><td>8,737</td><td>16,575</td><td>15,295</td></tr><tr><th>GR</th><td>0.132</td><td>0.147</td><td>0.083</td><td>0.148</td><td></td><td>500</td><td>1,180</td><td>1,399</td><td>8,339</td><td>8,474</td></tr><tr><th>AL</th><td>0.081</td><td>0.075</td><td>0.099</td><td>0.079</td><td>0.187</td><td></td><td>431</td><td>563</td><td>8,107</td><td>7,985</td></tr><tr><th>IT1</th><td>0.051</td><td>0.055</td><td>0.068</td><td>0.060</td><td>0.143</td><td>0.080</td><td></td><td>139</td><td>7,873</td><td>7,294</td></tr><tr><th>IT2</th><td>0.078</td><td>0.108</td><td>0.068</td><td>0.077</td><td>0.119</td><td>0.123</td><td>0.058</td><td></td><td>7,759</td><td>7,079</td></tr><tr><th>HI</th><td>0.108</td><td>0.114</td><td>0.117</td><td>0.115</td><td>0.143</td><td>0.110</td><td>0.097</td><td>0.101</td><td></td><td>7,677</td></tr><tr><th>VA</th><td>0.066</td><td>0.097</td><td>0.083</td><td>0.084</td><td>0.130</td><td>0.100</td><td>0.093</td><td>0.057</td><td>0.106</td><td></td></tr></tbody></table>

JP, Japan; CN, China; TH, Thailand; RE, La Réunion; GR, Greece; AL, Albania; IT1, Italy1/Cesena; IT2, Italy2/Brescia; HI, Hawaii; VA, Virginia (U.S.A).

* FST value which resulted not significantly different from zero at p>0.05 (ns) after Bonferroni correction.

doi:10.1371/journal.pntd.0005332.t003
<figure><img src="image_6.png" /><figcaption>**Fig 1.** Geographical representation of the co-ancestry distribution of *Ae. albopictus* mosquitoes. A total of 252 individuals from 10 populations were sampled from the supposed East Asian native area, and from newly colonized regions. The four colours represent the four hypothetical clusters as defined by STRUCTURE. The figure was based on freely available maps [93], and modified using Adobe Illustrator CC 2014 (Adobe Systems Inc., San Jose, CA, USA).</figcaption></figure>

doi:10.1371/journal.pntd.0005332.g001

Approximate Bayesian Computation (ABC method) to infer colonisation
histories

Five separate sequential ABC analyses (Table 4) were performed to disentangle the steps in the
colonization history: from the expansion in the putative Asian home range (analyses 1a-b-c) to
the invasion of La Réunion Island (analysis 2), North America (analysis 3) and Mediterranean
region (analyses 4–5). The graphical representation of the most-likely scenario obtained for each
analysis, along with the parameters inferred from genetic data, are presented in Figs 2 and 3.
Analysis 1, which consisted of three sequential steps (1a-b-c), considered the relationships among
the Asian samples collected in China, Thailand and Japan (Table 4, Fig 2). In analysis 1a, the
divergence among these populations has been assumed in the absence of admixture events. Here,
the highest posterior probability was for scenario 1 (P = 0.66) in which the first step of population
divergence occurred between China and Thailand, and a second split between China and Japan.
When admixture events were considered (analysis 1b), the highest posterior probability supported
scenario 4 (P = 0.58), in which Japan arose as an admixture between China and Thailand. Both
scenarios indicated China as an ancestral population. However, we cannot unambiguously dem-
onstrate that the derivation of Japan might include an admixture event between China and Thai-
land. Indeed, the comparison (analysis 1c) between the scenarios with and without admixture
resulted in a slightly higher posterior probability (P = 0.54) for the admixture hypothesis (scenario
<table><caption>Table 4. Posterior probabilities and 95% confidence intervals for the competing invasion scenarios of *Ae. albopictus* within native and newly colonised regions, as estimated by the ABC approach.</caption><thead><tr><th>Region</th><th>Analysis</th><th>Scenario</th><th>Posterior Probability</th><th>Confidence interval</th></tr></thead><tbody><tr><td rowspan="12">Home Range (East Asia)</td><td rowspan="3">1a</td><td>No Admixture</td><td>1 CN→TH; CN→JP</td><td>0.6623</td><td>[0.6007,0.7239]</td></tr><tr><td rowspan="2">1b</td><td>2 JP→CN; JP→TH</td><td>0.2703</td><td>[0.1422,0.3984]</td></tr><tr><td>3 TH→CN; TH→JP</td><td>0.0675</td><td>[0.0000,0.1826]</td></tr><tr><td rowspan="6">1c</td><td rowspan="6">No Admixture vs Admixture</td><td>4 CN→TH; CN+TH→JP</td><td>0.5762</td><td>[0.5219,0.6304]</td></tr><tr><td>5 CN→JP; CN+JP→TH</td><td>0.2522</td><td>[0.2045,0.2998]</td></tr><tr><td>6 TH→JP; TH+JP→CN</td><td>0.0454</td><td>[0.0283,0.0625]</td></tr><tr><td>7 JP→TH; JP+TH→CN</td><td>0.0613</td><td>[0.0428,0.0799]</td></tr><tr><td>8 JP→CN; JP+CN→TH</td><td>0.0649</td><td>[0.0484,0.0815]</td></tr><tr><td>1 CN→TH; CN→JP</td><td>0.4645</td><td>[0.3882,0.5407]</td></tr><tr><td colspan="2"></td><td>4 CN→TH; CN+TH→JP</td><td>0.5355</td><td>[0.4593,0.6118]</td></tr><tr><td rowspan="5">Newly Invaded Regions</td><td rowspan="5">La Réunion</td><td rowspan="5">2</td><td>1 CN→TH; CN+TH→JP; CN→RE</td><td>0.2477</td><td>[0.1002,0.3953]</td></tr><tr><td>2 CN→TH; CN+TH→JP; TH→RE</td><td>0.3846</td><td>[0.2421,0.5271]</td></tr><tr><td>3 CN→TH; CN+TH→JP; TH+JP→RE</td><td>0.2800</td><td>[0.1602,0.3998]</td></tr><tr><td>4 CN→TH; CN+TH→JP; JP+CN→RE</td><td>0.0253</td><td>[0.0000,0.1210]</td></tr><tr><td>5 CN→TH; CN+TH→JP; JP→RE</td><td>0.0624</td><td>[0.0000,0.1598]</td></tr><tr><td rowspan="20">Hawaii, North America, North Italy</td><td rowspan="20">3</td><td>1 CN→JP; CN→HI; CN→VA; CN→IT2</td><td>0.0008</td><td>[0.0000,0.0112]</td></tr><tr><td>2 CN→JP; CN→HI; JP→VA; CN→IT2</td><td>0.0003</td><td>[0.0000,0.0107]</td></tr><tr><td>3 CN→JP; CN→HI; CN→VA; JP→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>4 CN→JP; CN→HI; CN→VA; JP→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>5 CN→JP; CN→HI; HI→VA; CN→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>6 CN→JP; CN→HI; CN→VA; HI→IT2</td><td>0.0001</td><td>[0.0000,0.0106]</td></tr><tr><td>7 CN→JP; CN→HI; CN→VA; VA→IT2</td><td>0.0177</td><td>[0.0000,0.0394]</td></tr><tr><td>8 CN→JP; CN→HI; HI→VA; CN→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>9 CN→JP; CN→HI; JP→VA; VA→IT2</td><td>0.1069</td><td>[0.0197,0.1940]</td></tr><tr><td>10 CN→JP; CN→HI; HI→VA; VA→IT2</td><td>0.0017</td><td>[0.0000,0.0121]</td></tr><tr><td>11 CN→JP; CN→HI; JP→VA; CN→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>12 CN→JP; CN→HI; HI→VA; HI→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>13 CN→JP; CN→HI; HI→VA; VA→IT2</td><td>0.0055</td><td>[0.0000,0.0168]</td></tr><tr><td>14 CN→JP; CN→HI; HI→VA; JP→IT2</td><td>0.0000</td><td>[0.0000,0.0105]</td></tr><tr><td>15 CN→JP; CN→HI; CN→VA; VA→IT2</td><td>0.0008</td><td>[0.0000,0.0112]</td></tr><tr><td>16 CN→JP; CN→HI; JP→VA; HI→IT2</td><td>0.0005</td><td>[0.0000,0.0109]</td></tr><tr><td>17 CN→JP; CN→HI; JP→VA; VA→IT2</td><td>0.8609</td><td>[0.7703,0.9516]</td></tr><tr><td>18 CN→JP; CN→HI; JP→VA; HI→IT2</td><td>0.0047</td><td>[0.0000,0.0167]</td></tr><tr><td rowspan="3">Albania, Greece</td><td rowspan="3">4</td><td>1 CN→TH; CN→AL; TH→GR</td><td>1.0000</td><td>[0.9999,1.0000]</td></tr><tr><td>2 CN→TH; CN→AL; AL→GR</td><td>0.0000</td><td>[0.0000,0.0000]</td></tr><tr><td>3 CN→TH; CN→AL; CN→GR</td><td>0.0000</td><td>[0.0000,0.0000]</td></tr><tr><td rowspan="3">North Italy</td><td rowspan="3">5</td><td>1 CN→TH; CN+TH→JP; TH→RE; JP→VA; VA→IT2; RE→IT1</td><td>0.0034</td><td>[0.0000,0.0077]</td></tr><tr><td>2 CN→TH; CN+TH→JP; TH→RE; JP→VA; VA→IT2; IT2→IT1</td><td>0.0012</td><td>[0.0000,0.0029]</td></tr><tr><td>3 CN→TH; CN+TH→JP; TH→RE; JP→VA; VA→IT2; IT2+RE→IT1</td><td>0.9954</td><td>[0.9908,0.9999]</td></tr></tbody></table>

JP, Japan; CN, China; TH, Thailand; RE, La Réunion; GR, Greece; AL, Albania; IT1, Italy1/Cesena; IT2, Italy2/Brescia; HI, Hawaii; VA, Virginia (U.S.A).

doi:10.1371/journal.pntd.0005332.t004
Home Range

**Analysis 1a (no admixture)**

3 scenarios tested

Most likely scenario: 1

<figure><img src="image_7.png" /></figure>

Posterior probability (logistic regression): 0.6623

Confidence interval: [0.6007 - 0.7239]

**Analysis 1b (admixture)**

5 scenarios tested

Most likely scenario: 4

<figure><img src="image_8.png" /></figure>

r = 0.58

Posterior probability (logistic regression): 0.5762

Confidence interval: [0.5219 - 0.6304]

**Analysis 1c (no admixture versus admixture)**

Most likely scenario: 4

<figure><img src="image_9.png" /></figure>

Posterior probability (logistic regression): 0.5355

Confidence interval: [0.4593 - 0.6118]

**Fig 2.** Graphical representation of the most likely scenario of each set of scenarios describing the dynamics of samples within the native area using ABC methods.

doi:10.1371/journal.pntd.0005332.g002
Newly Invaded Regions

Analysis 2: Invasion of Réunion

5 scenarios tested

Most likely scenario: 2

<figure><img src="image_10.png" /></figure>

Posterior probability (logistic regression): 0.3846
Confidence interval: [0.2421 - 0.5271]

Bottleneck Réunion: 14 generations

Analysis 3: Invasion of Pacific Ocean
(Hawaii), USA (Virginia) and North Italy

18 scenarios tested

Most likely scenario: 17

<figure><img src="image_11.png" /></figure>

Posterior probability (logistic regression): 0.8609
Confidence interval: [0.7703 - 0.9516]

Bottleneck Pacific Ocean (Hawaii): 25 generations
Bottleneck USA (Virginia): 10 generations
Bottleneck Italy 2: 9 generations

Analysis 4: Invasion of Albania and Greece

3 scenarios tested

Most likely scenario: 1

<figure><img src="image_12.png" /></figure>

Posterior probability (logistic regression): 1.000
Confidence interval: [0.999 - 1.000]

Bottleneck Albania: 17 generations
Bottleneck Greece: 27 generations

Analysis 5: Invasion of North Italy

3 scenarios tested

Most likely scenario: 3

<figure><img src="image_13.png" /></figure>

Posterior probability (logistic regression): 0.9954
Confidence interval: [0.9908 - 0.999]

Bottleneck Italy 1: 4 generations
Bottleneck Italy 2: 10 generations
Bottleneck USA (Virginia): 8 generations
Bottleneck Réunion: 11 generations

Tropical areas: 12 - 17 generations per year
Temperate areas: 4 - 7 generations per year

**Fig 3.** Graphical representation of the most likely scenario of each set of scenarios describing the dynamics of samples in newly invaded areas using ABC methods.

doi:10.1371/journal.pntd.0005332.g003
4), which, however, had no strong statistical support. Indeed the 95% Confidence Intervals (CI) of the two scenarios ($P_{S4}$CI = [0.46, 0.61]; $P_{S1}$CI = [0.39, 0.54]) were partially overlapping.

In the subsequent four scenarios the origins of the adventive populations were considered.

Analysis 2 focused on the invasion of La Réunion Island. To determine which Asiatic population mostly contributed to the invasion of La Réunion, 5 alternative hypotheses were simulated (Table 4). Scenario 2, where La Réunion mosquitoes were derived from Thailand, presented the highest posterior probability (P = 0.3846, 95% CI [0.2421, 0.5271]) (Table 4; Fig 3). However, note that the 95% CI for this hypothesis overlapped with those of La Réunion's derivation from China and/or from an admixture between Thailand and Japan.

Analysis 3 focused on the colonization history of Hawaii, North America and North Italy (IT2-Brescia). A total of 18 scenarios were designed to test different hypotheses of introduction. The results unambiguously indicated scenario 17 (Table 4; Fig 3), in which the Hawaiian and North American (Virginia) mosquitoes were derived from Japan. North America acted as a 'bridgehead'[94] from which the mosquitoes were introduced into North Italy. The choice of this scenario was highly supported by a posterior probability (P = 0.8609, 95% CI [0.7703, 0.9516]) never overlapping with those of the other competing scenarios.

In analysis 4, the relationships among the nearby Mediterranean populations of Albania and Greece were considered together with their putative source, China and Thailand respectively, as suggested by the STRUCTURE data. The introduction of mosquitoes from China to Albania and from Thailand to Greece was supported by the highest posterior probability (P = 1.0000, 95% CI [0.9999, 1.0000]) never overlapping those of the other competing scenarios (Table 4; Fig 3).

Finally, we tried to disentangle the relationship between the two North Italian samples, IT1-Cesena and IT2-Brescia. Out of the three considered scenarios (Table 4), that with the most support (P = 0.9954, 95% CI [0.9908, 0.9999]) suggested a different origin for the two Italian samples: IT1 was the result of an admixture between La Réunion and individuals from IT2 (Table 4; Fig 3). The La Réunion source area shows a much higher contribution to the genetic pool of the IT1 (Cesena) sample, as the admixture rate was estimated to be 60%.

Interestingly, the computations of the bottleneck severity parameter from posterior distributions provided overall support for minimal to moderate bottleneck severity during the invasions with the duration of the bottlenecks varying from 4 (IT1, Cesena) to 27 generations (GR, Greece).

## Discussion

Our results clearly confirm that: 1) Southeast Asia, and especially China, is the native range of *Ae. albopictus*, 2) the invasion process is supported by a chaotic propagule distribution, which may play an important role in maintaining the genetic diversity and in the establishment of the adventive populations, and 3) the complex invasion pathways inferred from genetic data are congruent with the historical documentation concerning the presence of this species. All these outcomes are summarized in Fig 4.

## Genetic status of *Aedes albopictus* within East Asia

It has been suggested that, before the global invasion, the presence of climatically suitable areas across East Asia enabled rapid population growth and expansion allowing the persistence of this mosquito in widely interconnected populations from Southeast China to the Indochinese Peninsula [21, 54]. Recent changes in the environment created by urbanization allowed the species to become very abundant in rural and suburban/urban areas across East Asia [16, 95, 96]. Within these areas we sampled three high density populations, one from Southeast China
<figure><img src="image_14.png" /><figcaption>**Fig 4.** Demographic history of Asian, Indian Ocean, American and Mediterranean Basin populations as deduced by genetic and historical data.</figcaption></figure>

doi:10.1371/journal.pntd.0005332.g004

(Fujian province), one from Thailand (Lampang province) and one from South Japan (Nagasaki area) to investigate their ancestral relationships and their actual genetic status. Based on the analysis of the genetic variability, it appears clear that the samples from China, Thailand and Japan represent populations displaying genetic features expected in large populations in a native area, such as a high number of alleles, coupled with a high number of private alleles occurring at high frequencies [97]. This observation is especially interesting for the Chinese sample, which was collected in the Southeastern coastal region, one of the native areas of this mosquito [21]. The low levels of differentiation and the high co-ancestry values identified among China and the other two Asian samples allows us to confirm that, in the native range of expansion, these populations maintain high genetic connectivity, revealing their ancestral common origin. The conclusive ABC analysis indicates that, among the East Asian populations,
China appears to be the oldest population. Japan, in turn, has a high probability to represent a population emerging from an admixture between the more ancient populations from China and Thailand. A certain level of admixture may be caused by multiple introductions during the initial stages of invasion, which, in turn, may cause an increase in genetic diversity favouring expansion and adaptation [98, 99]. The historical networks of trade and exchanges across East Asian maritime space, the ‘China seas’ [100], may have facilitated the diffusion of propagules. It is noteworthy that our Japanese sample was collected in the Southwestern region, which represents an ancestral area in which the mosquito was well established for a long time, and from where it expanded its infestation range northwards [101, 102].

## Invasion processes out of East Asia

Outside East Asia, our data depict a picture of multiple migration patterns, with some erosion of variability. Historically, since the 18th century this mosquito is believed to have spread, along with humans, from Southeast Asia to Madagascar and the Indian Ocean islands [103]. As a consequence, in this region the mosquito had a long history of colonization and it has had time to adapt and probably to undergo differentiative processes [55]. Our La Réunion sample has the genetic signature of these processes: it maintains ancestry with the East Asian populations, but also displays the signs of differentiative processes. It is highly polymorphic with a great number of private and rare alleles, all features typical of an old and well established population.

After its spread to islands in the Indian Ocean, the reported invasion of this species to new areas started around 1900, when *Ae. albopictus* began a chaotic global expansion that continues to the present [104]. In the New World, the species was introduced into the Hawaiian islands at the end of the 18th century, probably from Japan [105, 106] and it became the primary mosquito species [107]. In continental USA, the first detection was in Texas in the 1980s, imported from Japan/Asia as a consequence of the complex ramifications of international business and the trade of tyres [68]. The mosquito spread to several USA states up the East Coast and North-Eastern region due to internal trade and multiple introductions from Japan/South Asia and Hawaii [108]. These historical records parallel our STRUCTURE and ABC data in corroborating the hypothesis that Japan, among the Asian samples, represented an important source area from which the mosquito was introduced into the USA.

In the Mediterranean region the outcomes of cluster analysis and scenario-testing portray a complex invasion pattern with most of the established populations being admixtures resulting from independent introductions scattered across time. The genetic discontinuity between the populations from geographically related countries, such as Albania, Greece and Italy, is a clear example. The Albanian population displays high co-ancestry with China from where it was introduced in 1979, when China was one of Albania's few trading partners [50, 109]. The Albanian infestation was the first recorded invasion outside the Oriental and Australasian regions and the first in the Mediterranean area. Although the mosquito became established in Albania, there were no reports in any other European country until 1990, when it was detected in Italy [110, 111], and only in 2003 in Greece [66]. The evolutionary scenarios modelled in ABC indicate the Greek population from the Athens area as an independent and recent derivation from Thailand. How this region could have been implicated in the introduction of this mosquito is an open question, although it is noteworthy that the Athens Piraeus Port is the hub for the international shipping trade from Thailand to the Mediterranean region [112]. Regarding the presence of *Ae. albopictus* in North Italy, our data clearly show that, although North America acted as an important 'bridgehead' [94], different and unrelated introductions contributed to the establishment of populations in North Italy. Indeed, while one of the two Italian samples, Brescia (IT2), shares high ancestry with the Americas, the other geographically close sample,
Cesena (IT1), appears to be the result of admixture events mainly with La Réunion. Cesena is an area of tourism in Emilia Romagna on the West coast of the Adriatic sea, with a port, which is on the cargo shipping route from La Réunion/Mauritius to Italy [113]. We cannot exclude the hypothesis that the introduction of La Réunion mosquitoes into the Cesena area may be correlated with this on-going shipping network.

## Global invasion of *Ae. albopictus* as a vector of arboviruses: what consequences for disease outbreaks?

It is likely that independent and also trans-continental introductions of *Ae. albopictus* may have facilitated the rapid establishment of adventive populations through admixture of unrelated genomes. As a consequence, a great amount of intra-population variability has been detected, and it is likely that this variability may extend to the genetic mechanisms controlling vector competence [114]. Intriguingly, competence for CHIKV, DENV and ZIKV has been found to be highly variable within and among populations [35, 38, 61, 63, 115]. Successful viral transmission is indeed strictly dependent on specific combinations of mosquito genome and viral genetic characteristics [61, 62]. Thus, in the context of the invasion process of this mosquito, it is possible that both population ancestry and admixture may contribute to create the conditions for the efficient transmission of arboviruses and for outbreak establishment. An example is the 2007 chikungunya outbreak in North Italy (the first in Europe), in an area comprising Cesena/Ravenna [45, 116]. This outbreak was caused by *Ae. albopictus* autochthonous transmission of the CHIKV strain carrying the A226V mutation [45, 117] previously identified during the large 2005–2006 outbreak in La Réunion island [118, 119]. Thus, these North Italian mosquitoes share similar high competence for the same CHIKV genotype with the La Réunion mosquitoes, with which, as we have shown, they have high co-ancestry.

These observations stimulate the need to explore how the dynamics of different mosquito genetic backgrounds, during the invasion processes, may have impacted variation in competence within and among the adventive populations. Moreover, we must consider that the spread of this mosquito occurred across different environmental conditions. Thus, the variation in competence between the eco-geographic populations may be the result of interactions between unexplored genetic factors and the complex environmental landscapes [25, 120]. Knowledge of these interactions is crucial in the assessment of the risk of arbovirus outbreaks in old and newly invaded areas where *Ae. albopictus* has become established.

## Supporting Information

S1 Fig. Relationship between pairwise FST-values and the geographical distances for the 10 *Ae. albopictus* populations.

(DOC)

S2 Fig. Three-dimensional plot of principal coordinate analysis based on similarity matrix derived from *Ae. albopictus* microsatellite data.

(TIF)

S1 Table. Definition and prior distribution of parameters used in the ABC analyses for describing the set of scenarios investigated for the reconstruction of the invasion of *Ae. albopictus*.

(DOC)

S2 Table. Number of alleles per population at each microsatellite locus.

(DOCX)
S3 Table. Average coefficient of ancestry obtained from a STRUCTURE run with K = 4 for 252 individuals of *Ae. albopictus* from 10 samples collected in different eco-geographical areas.

(DOC)

**Acknowledgments**

The study was performed within the FAO/IAEA research CRP programme “Exploring Genetic, Molecular, Mechanical and Behavioural Methods of Sex Separation in Mosquitoes” (FS). This research is in the frame of the University of Pavia strategic theme “Towards a governance model for international migration: an interdisciplinary and diachronic perspective (MIGRAT-IN-G)”.

**Author Contributions**

**Conceptualization:** ARM GG CRG MM AVR ABF MB LMG.

**Data curation:** MM AL CRG.

**Formal analysis:** MM CRG GG ARM FS.

**Funding acquisition:** ARM GG FS.

**Investigation:** MM AL GS.

**Methodology:** MM CRG.

**Project administration:** ARM GG.

**Resources:** PS.

**Supervision:** ARM GG CRG.

**Validation:** AL.

**Visualization:** ARM CRG MM FS LMG.

**Writing – original draft:** CRG ARM MM GG FS.

**Writing – review & editing:** MM CRG FS AVR ABF PS GS MB LMG ARM GG.

**References**

1. Lucy FE, Roy H, Simpson A, Carlton JT, Hanson JM, Magellan K, et al. INVASIVESNET towards an International Association for Open Knowledge on Invasive Alien Species. Manag Biol Invasion. 2016; 7(2):131–9.

2. Simberloff D, Martin JL, Genovesi P, Maris V, Wardle DA, Aronson J, et al. Impacts of biological invasions: what’s what and the way forward. Trends Ecol Evol. 2013; 28(1):58–66. doi: 10.1016/j.tree. 2012.07.013 PMID: 22889499

3. McCoy KD. The population genetic structure of vectors and our understanding of disease epidemiology. Parasite—J Soc Fr Parasitol. 2008; 15(3):444–8.

4. Jackson H, Strubbe D, Tollington S, Prys-Jones R, Matthysen E, Groombridge JJ. Ancestral origins and invasion pathways in a globally invasive bird correlate with climate and influences from bird trade. Mol Ecol. 2015; 24(16):4269–85. doi: 10.1111/mec.13307 PMID: 26172573

5. Roques L, Garnier J, Hamel F, Klein EK. Allee effect promotes diversity in traveling waves of colonization. Proc Natl Acad Sci U S A. 2012; 109(23):8828–33. doi: 10.1073/pnas.1201695109 PMID: 22611189

6. Simberloff D. The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst. 2009; 40:81–102.
7. Egizi A, Kiser J, Abadam C, Fonseca DM. The hitchhiker's guide to becoming invasive: exotic mosquitoes spread across a U.S. state by human transport not autonomous flight. Mol Ecol. 2016; 25(13):3033-47. doi: 10.1111/mec.13653 PMID: 27087539

8. Zalasiewicz J, Williams M, Steffen W, Crutzen P. The new world of the Anthropocene. Environ Sci Technol. 2010; 44(7):2228-31. doi: 10.1021/es903118j PMID: 20184359

9. Levine JM, Adler PB, HilleRisLambers J. On testing the role of niche differences in stabilizing coexistence. Funct Ecol. 2008; 22(5):934-6.

10. Wilkerson RC, Linton YM, Fonseca DM, Schultz TR, Price DC, Strickman DA. Making mosquito taxonomy useful: a stable classification of tribe *Aedini* that balances utility with current knowledge of evolutionary relationships. PLoS One. 2015; 10(7):e0133602. doi: 10.1371/journal.pone.0133602 PMID: 26226613

11. Malacrida AR, Gomulski LM, Bonizzoni M, Bertin S, Gasperi G, Guglielmino CR. Globalization and fruitfly invasion and expansion: the medfly paradigm. Genetica. 2007; 131(1):1-9. doi: 10.1007/s10709-006-9117-2 PMID: 17111234

12. Tabachnick W. Evolutionary genetics and arthropod-borne disease. Yellow fever mosquito. Am Entomol. 1991; 37:14-24.

13. Brown JE, Evans BR, Zheng W, Obas V, Barrera-Martinez L, Egizi A, et al. Human impacts have shaped historical and recent evolution in *Aedes aegypti*, the dengue and yellow fever mosquito. Evolution. 2014; 68(2):514-25. doi: 10.1111/evo.12281 PMID: 24111703

14. Kraemer MU, 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(150035).

15. Kaufman MG, Fonseca DM. Invasion biology of *Aedes japonicus japonicus* (Diptera: Culicidae). Annu Rev Entomol. 2014; 59:31-49. doi: 10.1146/annurev-ento-011613-162012 PMID: 24397520

16. Paupy C, Delatte H, Bagny L, Corbel V, Fontenille D. *Aedes albopictus*, an arbovirus vector: from the darkness to the light. Microb Infect. 2009; 11(14-15):1177-85.

17. Waldock J, Chandra NL, Lelieveld J, Proestos Y, Michael E, Christophides G, et al. The role of environmental variables on *Aedes albopictus* biology and chikungunya epidemiology. Pathog Glob Health. 2013; 107(5):224-41. doi: 10.1179/2047773213Y.0000000100 PMID: 23916332

18. Reiter P, Lathrop S, Bunning M, Biggerstaff B, Singer D, Tiwari T, et al. Texas lifestyle limits transmission of dengue virus. Emerging Infect Dis. 2003; 9(1):86-9. doi: 10.3201/eid0901.020220 PMID: 12533286

19. Baer B, Schmid-Hempel P. Unexpected consequences of polyandry for parasitism and fitness in the bumblebee, *Bombus terrestris*. Evolution. 2001; 55(8):1639-43. PMID: 11580023

20. Smith CEG. The history of dengue in tropical Asia and its probable relationship to the mosquito *Aedes aegypti*. Am J Trop Med Hy. 1956; 59:243-51.

21. Mogi M, Armbruster P, Tuno N, Campos R, Eritja R. Simple indices provide insight to climate attributes delineating the geographic range of *Aedes albopictus* (Diptera: Culicidae) prior to worldwide Invasion. J Med Entomol. 2015; 52(4):647-57. doi: 10.1093/jme/tjv038 PMID: 26335471

22. Delatte H, Gimonneau G, Triboire A, Fontenille D. Influence of temperature on immature development, survival, longevity, fecundity, and gonotrophic cycles of *Aedes albopictus*, vector of chikungunya and dengue in the Indian Ocean. J Med Entomol. 2009; 46(1):33-41. PMID: 19198515

23. Medlock JM, Hansford KM, Schaffner F, Versteirt V, Hendrickx G, Zeller H, et al. A review of the invasive mosquitoes in Europe: ecology, public health risks, and control options. Vector Borne Zoonotic Dis. 2012; 12(6):435-47. doi: 10.1089/vbz.2011.0814 PMID: 22448724

24. Carvalho RG, Lourenco-de-Oliveira R, Braga IA. Updating the geographical distribution and frequency of *Aedes albopictus* in Brazil with remarks regarding its range in the Americas. Mem Inst Oswaldo Cruz. 2014; 109(6):787-96. doi: 10.1590/0074-0276140304 PMID: 25317707

25. Lambrechts L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, et al. Impact of daily temperature fluctuations on dengue virus transmission by *Aedes aegypti*. Proc Natl Acad Sci U S A. 2011; 108(18):7460-5. doi: 10.1073/pnas.1101377108 PMID: 21502510

26. Cornel AJ, Hunt RH. *Aedes albopictus* in Africa? First records of live specimens in imported tires in Cape Town. J Am Mosq Control Assoc. 1991; 7(1):107-8. PMID: 2045799

27. Guisan A, Petitpierre B, Broennimann O, Daehler C, Kueffer C. Unifying niche shift studies: insights from biological invasions. Trends Ecol Evol. 2014; 29(5):260-9. doi: 10.1016/j.tree.2014.02.009 PMID: 24656621

28. Costanzo KS, Schelble S, Jerz K, Keenan M. The effect of photoperiod on life history and blood-feeding activity in *Aedes albopictus* and *Aedes aegypti* (Diptera: Culicidae). J Vector Ecol. 2015; 40(1):164-71. doi: 10.1111/jvec.12146 PMID: 26047197
29. Brady OJ, Golding N, Pigott DM, Kraemer MUG, Messina JP, Reiner RC, et al. Global temperature constraints on *Aedes aegypti* and *Ae. albopictus* persistence and competence for dengue virus transmission. Parasit Vectors. 2014; 7(338):1–17.

30. Poelchau MF, Reynolds JA, Elsik CG, Denlinger DL, Armbruster PA. Deep sequencing reveals complex mechanisms of diapause preparation in the invasive mosquito, *Aedes albopictus*. Proc Biol Sci. 2013; 280(1759):1–9.

31. Urbanski J, Mogi M, O'Donnell D, DeCotiis M, Toma T, Armbruster P. Rapid adaptive evolution of photoperiodic response during invasion and range expansion across a climatic gradient. Am Nat. 2012; 179(4):490–500. doi: 10.1086/664709 PMID: 22437178

32. Lacour G, Chanaud L, L'Ambert G, Hance T. Seasonal synchronization of diapause phases in *Aedes albopictus* (Diptera: Culicidae). PLoS One. 2015; 10(12):e0145311. doi: 10.1371/journal.pone.0145311 PMID: 26683460

33. Gasperi G, Bellini R, Malacrida AR, Crisanti A, Dottori M, Aksoy S. A new threat looming over the Mediterranean basin: emergence of viral diseases transmitted by *Aedes albopictus* mosquitoes. PLoS Negl Trop Dis. 2012; 6(9):e1836. doi: 10.1371/journal.pntd.0001836 PMID: 23029593

34. Bonizzoni M, Gasperi G, Chen X, James AA. The invasive mosquito species *Aedes albopictus*: current knowledge and future perspectives. Trends Parasitol. 2013; 29(9):460–8. doi: 10.1016/j.pt.2013.07.003 PMID: 23916878

35. Vega-Rua A, Lourenco-de-Oliveira R, Mousson L, Vazeille M, Fuchs S, Yebakima A, et al. Chikungunya virus transmission potential by local *Aedes* mosquitoes in the Americas and Europe. PLoS Negl Trop Dis. 2015; 9(5):e0003780. doi: 10.1371/journal.pntd.0003780 PMID: 25993633

36. Wong PSJ, Li MZI, Chong CS, Ng LC, Tan CH. *Aedes* (*Stegomyia*ia) *albopictus* (Skuse): a potential vector of Zika virus in Singapore. PLoS Negl Trop Dis. 2013; 7(8):e2348. doi: 10.1371/journal.pntd.0002348 PMID: 23936579

37. Gratz NG. Critical review of the vector status of *Aedes albopictus*. Med Vet Entomol. 2004; 18(3):215–27. doi: 10.1111/j.0269-283X.2004.00513.x PMID: 15347388

38. 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(3):1–11.

39. Grard G, Caron M, Mombo IM, Nkoghe D, Mboui Ondo S, Jiolle D, et al. Zika virus in Gabon (Central Africa)-2007: a new threat from *Aedes albopictus*? PLoS Negl Trop Dis. 2014; 8(2):e2681. doi: 10.1371/journal.pntd.0002681 PMID: 24516683

40. Jupille H, Seixas G, Mousson L, Sousa CA, Failloux AB. Zika Virus, a new threat for Europe? PLoS Negl Trop Dis. 2016; 10(8):e0004901. doi: 10.1371/journal.pntd.0004901 PMID: 27505002

41. Reiter P. Control of urban Zika vectors: should we return to the successful PAHO/WHO strategy? PLoS Negl Trop Dis. 2016; 10(6):e0004769. doi: 10.1371/journal.pntd.0004769 PMID: 27249063

42. Akiner MM, Demirci B, Babuadze G, Robert V, Schaffner F. Spread of the invasive mosquitoes *Aedes aegypti* and *Aedes albopictus* in the Black Sea region increases risk of Chikungunya, Dengue, and Zika outbreaks in Europe. PLoS Negl Trop Dis. 2016; 10(4):e0004664. doi: 10.1371/journal.pntd.0004664 PMID: 27115737

43. Morrison TE. Reemergence of chikungunya virus. J Virol. 2014; 88(20):11644–7. doi: 10.1128/JVI.01432-14 PMID: 25078691

44. Suter TT, Flacio E, Feijoo Farina B, Engeler L, Tonolla M, Regis LN, et al. Surveillance and control of *Aedes albopictus* in the Swiss-Italian border region: differences in egg densities between intervention and non-intervention areas. PLoS Negl Trop Dis. 2016; 10(1):e0004315. doi: 10.1371/journal.pntd.0004315 PMID: 26734946

45. Rezza G, Nicoletti L, Angelini R, Romi R, Finarelli AC, Panning M, et al. Infection with chikungunya virus in Italy: an outbreak in a temperate region. Lancet. 2007; 370(9602):1840–6. doi: 10.1016/S0140-6736(07)61779-6 PMID: 18061059

46. Angelini P, Macini P, Finarelli AC, Pol C, Venturelli C, Bellini R, et al. Chikungunya epidemic outbreak in Emilia-Romagna (Italy) during summer 2007. Parassitologia. 2008; 50(1–2):97–8. PMID: 18693568

47. Gould EA, Gallian P, De Lamballerie X, Charrel RN. First cases of autochthonous dengue fever and chikungunya fever in France: from bad dream to reality! Clin Microbiol Infect. 2010; 16(12):1702–4. doi: 10.1111/j.1469-0691.2010.03386.x PMID: 21040155

48. Gjenero-Margan I, Aleraj B, Krajcar D, Lesnikar V, Klobucar A, Pem-Novosel I, et al. Autochthonous dengue fever in Croatia, August-September 2010. Euro Surveill. 2011; 16(9):pii = 19805.

49. Medley KA, Jenkins DG, Hoffman EA. Human-aided and natural dispersal drive gene flow across the range of an invasive mosquito. Mol Ecol. 2015; 24(2):284–95. doi: 10.1111/mec.12925 PMID: 25230113
50. Enserink M. A mosquito goes global. Science. 2008; 320(5878):864-6. doi: 10.1126/science.320.5878.864 PMID: 18487167

51. Black WC, McLain DK, Rai KS. Patterns of variation in the rDNA cistron within and among world populations of a mosquito, *Aedes albopictus* (Skuse). Genetics. 1989; 121(3):539-50. PMID: 2714638

52. Mousson L, Dauga C, Garrigues T, Schaffner F, Vazeille M, Failloux AB. Phylogeography of *Aedes* (*Stegomyia*) *aegypti* (L.) and *Aedes* (*Stegomyia*) *albopictus* (Skuse) (Diptera: Culicidae) based on mitochondrial DNA variations. Genet Res. 2005; 86(1):1-11. doi: 10.1017/S00166672305007627 PMID: 16181519

53. Beebe NW, Ambrose L, Hill LA, 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(8):e2361. doi: 10.1371/journal.pntd.0002361 PMID: 23951380

54. Porretta D, Mastrantonio V, Bellini R, Somboon P, Urbanelli S. Glacial history of a modern invader: phylogeography and species distribution modelling of the Asian tiger mosquito *Aedes albopictus*. PLoS One. 2012; 7(9).

55. Delatte H, Toty C, Boyer S, Bouetard A, Bastien F, Fontenille D. Evidence of habitat structuring *Aedes albopictus* populations in Reunion Island. PLoS Negl Trop Dis. 2013; 7(3):e2111. doi: 10.1371/journal.pntd.0002111 PMID: 23556012

56. Zhong DB, Lo E, Hu RJ, Metzger ME, Cummings R, Bonizzoni M, et al. Genetic analysis of invasive *Aedes albopictus* populations in Los Angeles county, California and its potential public health impact. PLoS One. 2013; 8(7):e68586. doi: 10.1371/journal.pone.0068586 PMID: 23861921

57. Kamgang B, Ngoagouni C, Manirakiza A, Nakoune E, Paupy C, Kazanji M. Temporal patterns of abundance of *Aedes aegypti* and *Aedes albopictus* (Diptera: Culicidae) and mitochondrial DNA analysis of *Ae. albopictus* in the Central African Republic. PLoS Negl Trop Dis. 2013; 7(12):e2590. doi: 10.1371/journal.pntd.0002590 PMID: 24349596

58. Ngoagouni C, Kamgang B, Nakoune E, Paupy C, Kazanji M. Invasion of *Aedes albopictus* (Diptera: Culicidae) into central Africa: what consequences for emerging diseases? Parasit Vectors. 2015; 8(191):1-7.

59. 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(3):125-34. doi: 10.1038/hdy.2016.35 PMID: 27273325

60. 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. doi: 10.1186/s13071-015-0794-5 PMID: 25890257

61. Vega-Rua A, Zouache K, Girod R, Failloux AB, Lourenco-de-Oliveira R. High level of vector competence of *Aedes aegypti* and *Aedes albopictus* from ten American countries as a crucial factor in the spread of Chikungunya virus. J Virol. 2014; 88(11):6294-306. doi: 10.1128/JVI.00370-14 PMID: 24672026

62. Vazeille M, Moutailler S, Coudrier D, Rousseaux C, Khun H, Huerre M, et al. Two Chikungunya isolates from the outbreak of La Reunion (Indian Ocean) exhibit different patterns of infection in the mosquito, *Aedes albopictus*. PLoS One. 2007; 2(11):e1168. doi: 10.1371/journal.pone.0001168 PMID: 18000540

63. Vazeille M, Zouache K, Vega-Rua A, Thiberge JM, Caro V, Yebakima A, et al. Importance of mosquito "quasispecies" in selecting an epidemic arthropod-borne virus. Sci Rep. 2016; 6:29564. doi: 10.1038/srep29564 PMID: 27383735

64. Gavaudan S, Duranti A, Barchiesi F, Ruschioni S, Antognini E, Calandri E, et al. Seasonal monitoring of *Aedes albopictus*: practical applications and outcomes. Vet Ital. 2014; 50(2):109-16. doi: 10.12834/Vetlt.1009.05 PMID: 24395672

65. Rueda LM. Pictorial keys for the identification of mosquitoes (Diptera: Culicidae) associated with dengue virus transmission. Zootaxa. 2004; 589:1-60.

66. Samanidou-Voyadjoglou A, Patsoula E, Spanakos G, Vakalis NC. Confirmation of *Aedes albopictus* (Skuse) (Diptera: Culicidae) in Greece. Eur Mosq Bull. 2005; 19:10-2.

67. Giatropoulos A, Emmanuel N, Koliopoulos G, Michaelakis A. A study on distribution and seasonal abundance of *Aedes albopictus* (Diptera: Culicidae) population in Athens, Greece. J Med Entomol. 2012; 49(2):262-9. PMID: 22493842

68. Kuno G. Revisiting Houston and Memphis: the background histories behind the discovery of the infestation by *Aedes albopictus* (Diptera: Culicidae) in the United States and their significance in the contemporary research. J Med Entomol. 2012; 49(6):1163-76. PMID: 23270144
69. Hawley WA, Reiter P, Copeland RS, Pumpuni CB, Craig GB Jr. *Aedes albopictus* in North America: probable introduction in used tires from northern Asia. Science. 1987; 236(4805):1114-6. PMID: 3576225

70. Hoban S, Gaggiotti O, Bertorelle G, Arntzen P, Bryja J, Fernandes M, et al. Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples. Methods Ecol Evol. 2013; 4(3):299-303.

71. Matschiner M, Salzburger W. TANDEM: integrating automated allele binning into genetics and genomics workflows. Bioinformatics. 2009; 25(15):1982-3. doi: 10.1093/bioinformatics/btp303 PMID: 19420055

72. Peakall R, Smouse PE. GenAIEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012; 28(19):2537-9. doi: 10.1093/bioinformatics/bts460 PMID: 22820204

73. Peakall R, Smouse PE. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006; 6(1):288-95.

74. Goudet J. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Available: http://www2.unil.ch/popgen/softwares/fstat.htm. 2001.

75. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984; 38:1358-70.

76. Dieringer D, Schlotterer C. MICROSATELLITE ANALYSER (MSA): a platform independent analysis tool for large microsatellite data sets. Mol Ecol Notes. 2003; 3(1):167-9.

77. Raymond M, Rousset F. Genepop (Version-1.2)—Population-genetics software for exact tests and ecumenicism. J Hered. 1995; 86(3):248-9.

78. Rousset F. genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Mol Ecol Resour. 2008; 8(1):103-6. doi: 10.1111/j.1471-8286.2007.01931.x PMID: 21585727

79. Rice WR. Analyzing tables of statistical tests. Evolution. 1989; 43(1):223-5.

80. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol. 2007; 16(5):1099-106. doi: 10.1111/j.1365-294X.2007.03089.x PMID: 17305863

81. Falush D, Stephens M, Pritchard JK. Inference of population structure: Extensions to linked loci and correlated allele frequencies. Genetics. 2003; 164:1567-87. PMID: 12930761

82. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes. 2007; 7(4):574-8. doi: 10.1111/j.1471-8286.2007.01758.x PMID: 18784791

83. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155:945-59. PMID: 10835412

84. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611-20. doi: 10.1111/j.1365-294X.2005.02553.x PMID: 15969739

85. Earl DA, Vonholdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012; 4(2):359-61.

86. Jakobsson M, Rosenberg NA. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007; 23(14):1801-6. doi: 10.1093/bioinformatics/btm233 PMID: 17485429

87. Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004; 4(1):137-8.

88. Cornuet JM, Pudlo P, Veyssier J, Dehne-Garcia A, Gautier M, Leblois R, et al. DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics. 2014; 30(8):1187-9. doi: 10.1093/bioinformatics/btt763 PMID: 24389659

89. Alto BW, Juliano SA. Temperature effects on the dynamics of *Aedes albopictus* (Diptera: Culicidae) populations in the laboratory. J Med Entomol. 2001; 38(4):548-56. PMID: 11476335

90. Garza JC, Williamson EG. Detection of reduction in population size using data from microsatellite loci. Mol Ecol. 2001; 10(2):305-18. PMID: 11298947

91. Cornuet JM, Santos F, Beaumont MA, Robert CP, Marin JM, Balding DJ, et al. Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation. Bioinformatics. 2008; 24(23):2713-9. doi: 10.1093/bioinformatics/btn514 PMID: 18842597
92. Cornuet JM, Ravigne V, Estoup A. Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinformatics. 2010; 11:401. doi: 10.1186/1471-2105-11-401 PMID: 20667077

93. Dalet D. d-maps 2016 [free map website]. Available from: http://d-maps.com/carte.php?num_car=2233&lang=en http://d-maps.com/carte.php?num_car=55&lang=en http://d-maps.com/carte.php?num_car=1405&lang=en http://d-maps.com/carte.php?num_car=6911&lang=en http://d-maps.com/carte.php?num_car=3251&lang=en http://d-maps.com/carte.php?num_car=3061&lang=en.

94. Lombaert E, Guillemaud T, Cornuet J-M, Malausa T, Facon B. Bridgehead effect in the worldwide invasion of the biocontrol harlequin ladybird. PLoS One. 2010; 5:e9743. doi: 10.1371/journal.pone.0009743 PMID: 20305822

95. Delatte H, Desvars A, Bouetard A, Bord S, Gimonneau G, Vourc'h G, et al. Blood-feeding behavior of *Aedes albopictus*, a vector of Chikungunya on La Reunion. Vector Borne Zoonotic Dis. 2010; 10 (3):249-58. doi: 10.1089/vbz.2009.0026 PMID: 19589060

96. 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 (11):e3301. doi: 10.1371/journal.pntd.0003301 PMID: 25393814

97. Khamis FM, Karam N, Ekesi S, M DEM, Bonomi A, Gomulski LM, et al. Uncovering the tracks of a recent and rapid invasion: the case of the fruit fly pest *Bactrocera invadens* (Diptera: Tephritidae) in Africa. Mol Ecol. 2009; 18(23):4798-810. doi: 10.1111/j.1365-294X.2009.04391.x PMID: 19821903

98. Kolar CS, Lodge DM. Progress in invasion biology: predicting invaders. Trends Ecol Evol. 2001; 16 (4):199-204. PMID: 11245943

99. Lockwood JL, Cassey P, Blackburn T. The role of propagule pressure in explaining species invasions. Trends Ecol Evol. 2005; 20(5):223-8. doi: 10.1016/j.tree.2005.02.004 PMID: 16701373

100. Schottenhammer A. The “China Seas” in world history: A general outline of the role of Chinese and East Asian maritime space from its origins to c. 1800. J Mar Isl Cult. 2012; 1:63-86.

101. Hotta S. Dengue vector mosquitoes in Japan: the role of *Aedes albopictus* and *Aedes aegypti* in the 1942–1944 dengue epidemics of Japanese Main Islands [in Japanese with English summary]. Med Entomol Zool. 1998; 49:267-74.

102. Oki M, Yamamoto T. Simulation of the probable vector density that caused the Nagasaki dengue outbreak vectored by *Aedes albopictus* in 1942. Epidemiol Infect. 2013; 141(12):2612-22. doi: 10.1017/S0950268813000447 PMID: 23481094

103. Paupy C, Girod R, Salvan M, Rodhain F, Failloux AB. Population structure of *Aedes albopictus* from La Reunion Island (Indian Ocean) with respect to susceptibility to a dengue virus. Heredity. 2001; 87 (Pt 3):273-83. PMID: 11737274

104. 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(5):e646. doi: 10.1371/journal.pntd.0000646 PMID: 20520794

105. Rai KS. *Aedes albopictus* in the Americas. Annu Rev Entomol. 1991; 36:459-84. doi: 10.1146/annurev.en.36.010191.002331 PMID: 2006869

106. Reiter P, Sprenger D. The used tire trade: a mechanism for the worldwide dispersal of container breeding mosquitoes. J Am Mosq Control Assoc. 1987; 3(3):494-501. PMID: 2904963

107. Leong MKH, Grace JK. Occurrence and distribution of mosquitoes (Diptera: Culicidae) of public health importance on the island of Oahu. Proc Hawaii Entomol Soc. 2009; 41:57-70.

108. Centers for Disease Control and Prevention. Surveillance and Control of *Aedes aegypti* and *Aedes albopictus* in the United States. Available: http://www.cdc.gov/chikungunya/resources/vector-control.html.

109. Adhami J, Reiter P. Introduction and establishment of *Aedes (Stegomyia) albopictus* skuse (Diptera: Culicidae) in Albania. J Am Mosq Control Assoc. 1998; 14(3):340-3. PMID: 9813831

110. Sabatini A, Raineri V, Trovato G, Coluzzi M. *Aedes albopictus* in Italy and possible diffusion of the species into the Mediterranean area. Parassitologia. 1990; 32(3):301-4. PMID: 2132441

111. Romi R. History and updating on the spread of *Aedes albopictus* in Italy. Parassitologia. 1995; 37(2-3):99-103. PMID: 8778671

112. ZIM Integrated Shipping Services Ltd. Available: http://www.zim.com/countrysites/asia/thailand/en-us/pages/zimlinenetwork.aspx?trade=5&line=PSS&Service=MSL.

113. Cargo Router. Route alternatives for shipping cargo from Bambous Virieux, Mauritius to Cesena, Italy. Available: http://www.cargorouter.com/freight-shipping/Mauritius/Bambous-Virieux/Italy/Cesena/.

114. Tabachnick WJ. Nature, nurture and evolution of intra-species variation in mosquito arbovirus transmission competence. Int J Env Res Public Health. 2013; 10(1):249-77.
115. Talbalaghi A, Moutailler S, Vazeille M, Failloux AB. Are *Aedes albopictus* or other mosquito species from northern Italy competent to sustain new arboviral outbreaks? Med Vet Entomol. 2010; 24(1):83-7. doi: 10.1111/j.1365-2915.2009.00853.x PMID: 20377735

116. Angelini R, Finarelli AC, Angelini P, Po C, Petropulacos K, Silvi G, et al. Chikungunya in north-eastern Italy: a summing up of the outbreak. Euro Surveill. 2007; 12(47):pii = 3313.

117. Bordi L, Carletti F, Castilletti C, Chiappini R, Sambri V, Cavrini F, et al. Presence of the A226V mutation in autochthonous and imported Italian chikungunya virus strains. Clin Infect Dis. 2008; 47(3):428-9. doi: 10.1086/589925 PMID: 18605910

118. Schuffenecker I, Iteman I, Michault A, Murri S, Frangeul L, Vaney MC, et al. Genome microevolution of chikungunya viruses causing the Indian Ocean outbreak. PLoS Med. 2006; 3(7):e263. doi: 10.1371/journal.pmed.0030263 PMID: 16700631

119. Tsetsarkin KA, Vanlandingham DL, McGee CE, Higgs S. A single mutation in chikungunya virus affects vector specificity and epidemic potential. PLoS Path. 2007; 3(12):e201.

120. Lefevre T, Vantaux A, Dabire KR, Mouline K, Cohuet A. Non-genetic determinants of mosquito competence for malaria parasites. PLoS Path. 2013; 9(6):e1003365.