Research Article
Print
Research Article
Population genetics of an invasive mosquito vector, Aedes albopictus in the Northeastern USA
expand article infoAndrea Gloria-Soria‡§, Talya Shragai|, Alexander T. Ciota, Todd B. Duval#, Barry W. Alto¤, Ademir J. Martins«», Kathleen M. Westby˄, Kim A. Medley˄, Isik Unlu˅, Scott R. Campbell¦, Malgorzata Kawalkowskiˀ, Yoshio Tsudaˁ, Yukiko Higaˁ, Nicholas Indelicato₵, Paul T. Leisnhamℓ, Adalgisa Caccone§, Philip M. Armstrong‡
‡ Center for Vector Biology & Zoonotic Diseases, Department of Entomology, New Haven, United States of America
§ Yale University, New Haven, United States of America
| Cornell University, Ithaca, United States of America
¶ Arbovirus Laboratory, Wadsworth Center, Slingerlands, United States of America
# Bristol County Mosquito Control Project, Attleboro, United States of America
¤ University of Florida, Vero Beach, United States of America
« Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
» Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Rio de Janeiro, Brazil
˄ Washington University in Saint Louis, Eureka, United States of America
˅ Miami-Dade Mosquito Control Division, Miami, United States of America
¦ Arthropod-Borne Disease Laboratory, Suffolk County Department of Health Services, Yaphank, United States of America
ˀ Division of Vector Control, Suffolk County Department of Health Services, Yaphank, United States of America
ˁ Department of Medical Entomology, National Institute of Infectious Diseases, Tokyo, Japan
₵ D.O.T. & I / Highway Division, Ewing, United States of America
ℓ University of Maryland, College Park, United States of America

Corresponding author: Andrea Gloria-Soria ( andrea.gloria-soria@ct.gov )

Academic editor: Deepa Pureswaran
© 2022 Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Gloria-Soria A, Shragai T, Ciota AT, Duval TB, Alto BW, Martins AJ, Westby KM, Medley KA, Unlu I, Campbell SR, Kawalkowski M, Tsuda Y, Higa Y, Indelicato N, Leisnham PT, Caccone A, Armstrong PM (2022) Population genetics of an invasive mosquito vector, Aedes albopictus in the Northeastern USA. NeoBiota 78: 99-127. https://doi.org/10.3897/neobiota.78.84986
Open Access

Abstract

The Asian tiger mosquito (Aedes albopictus) arrived in the USA in the 1980’s and rapidly spread throughout eastern USA within a decade. The predicted northern edge of its overwintering distribution on the East Coast of the USA roughly falls across New York, Connecticut, and Massachusetts, where the species has been recorded as early as 2000. It is unclear whether Ae. albopictus populations have become established and survive the cold winters in these areas or are recolonized every year. We genotyped and analyzed populations of Ae. albopictus from the northeast USA using 15 microsatellite markers and compared them with other populations across the country and to representatives of the major global genetic clades to investigate their connectivity and stability. Founder effects or bottlenecks were rare at the northern range of the Ae. albopictus distribution in the northeastern USA, with populations displaying high levels of genetic diversity and connectivity along the East Coast. There is no evidence of population turnover in Connecticut during the course of three consecutive years, with consistent genetic structure throughout this period. Overall, these results support the presence of established populations of Ae. albopictus in New York, Connecticut, and Massachusetts, successfully overwintering and migrating in large numbers. Given the stability and interconnectedness of these populations, Ae. albopictus has the potential to continue to proliferate and expand its range northward under mean warming conditions of climate change. Efforts to control Ae. albopictus in these areas should thus focus on vector suppression rather than eradication strategies, as local populations have become firmly established and are expected to reemerge every summer.

Keywords

Asian tiger mosquito, colonization, container-breeder, invasion genetics, propagule pressure, range expansion

Introduction

The Asian tiger mosquito (Aedes albopictus) is a highly invasive species that spread from its native range in East Asia to more than 50 countries on every continent, except Antarctica, during the last 40 years (Sprenger and Wuithiranyagool 1986; Kraemer et al. 2015). The global range expansion and success of this species has been propelled largely by human migration, transportation, and global commerce. Ae. albopictus lays desiccation-resistant eggs and develops in artificial water-holding containers, which facilitate its dispersal and establishment in urban and suburban environments (Sprenger and Reiter 1987; Hawley et al. 1987; Parker et al. 2020). Although Ae. albopictus feeds opportunistically on a wide range of species (Niebylski et al. 1994; Delatte et al. 2010) it can be an aggressive human biter and a vector of emergent human arboviruses including dengue, chikungunya, and Zika viruses (Metselaar et al. 1980; Gratz 2004; Paupy et al. 2012; Gloria-Soria et al. 2021). This raises the concern that the risk of these arboviruses will increase as this species proliferates and expands its geographic range, as observed in the Indian Ocean Islands, Italy, France, Japan, and Hawaii (Paupy et al. 2009; Grandadam et al. 2011; Rezza 2012).

In the continental USA, Ae. albopictus has been detected in 40 states, since the first population was discovered in Houston Texas in 1985 (Sprenger and Wuithiranyagool 1986; Hahn et al. 2017). However, many of these state records could represent transient seasonal introductions rather than established populations. Aedes albopictus has become established in southern California and much of the eastern half of the country (Linthicum et al. 2003; Kraemer et al. 2015), with populations continuing to move northward. The northern boundary for overwintering populations has been suggested to be at the isotherm of the coldest month mean temperature of 0 °C based on its distribution in Asia (Nawrocki and Hawley 1987) or isotherms with mean annual temperatures above 11 °C (Kobayashi et al. 2002). This corresponds roughly to southern New England and New York, where Ae. albopictus populations emerge annually, with the boundary expected to shift north due to a warming climate (Rochlin et al. 2013). Ae. albopictus was first detected in New York (NY) in 2000, in New York City and neighboring Long Island counties in 2003, and is currently spreading north into the Hudson Valley (Kulasekera et al. 2001; Rochlin et al. 2013; Hahn et al. 2016; Kache et al. 2020). In Connecticut (CT), this species was first detected in 2003 and then in 2006 (Andreadis et al. 2005; Andreadis 2009; Armstrong et al. 2017), and has been reported every year since 2010 during continuous statewide mosquito surveillance (Armstrong et al. 2017). Collections occur primarily along the southern margin of CT and successful overwintering of a local population was documented in 2013, during one of the four winters sampled (Armstrong et al. 2017).

We performed population genetic analyses on Ae. albopictus collected from NY, CT, and Massachusetts (MA), and compared them to established populations from other USA states and countries to better understand the process of mosquito colonization at the northern expansion front. Collections include mosquitoes sampled from 23 locations along the USA eastern seaboard from Florida to MA, one population from California and temporal collections at four locations in CT spanning three consecutive years. In addition, we include collections from Thailand, Japan, and Brazil as representatives of the major global genetic clusters identified in this species (Kotsakiozi et al. 2017). Here, we characterize the genetic diversity and genetic structure of Ae. albopictus populations in the Northeast USA, and evaluate the stability of populations in CT as representatives of the northern edge of Ae. albopictus distribution in the USA East Coast; seeking to understand the patterns of Ae. albopictus range expansion and establishment in the country. Based on classic invasion theory (Nei et al. 1975; Sakai et al. 2001), we predict low diversity at the northeastern invasion front (CT, NY, MA) relative to the south and the native range, with diversity in the Northeast declining gradually with latitude and evidence of recent bottlenecks consequence of founder events. Furthermore, if these populations have become established we expect stability in their genetic structure over multiple years.

Methods

Collections

A total of 1,342 Ae. albopictus mosquitoes were sent to the Connecticut Agricultural Experiment Station from Departments of Public Health, Mosquito Abatement Districts, and collaborators. All individuals were received as adults directly from the field, with the exception of four sampling sites that were collected as larvae. Larvae from Tappan, NY were reared and underwent one generation in the laboratory, larvae from Fire Island and Spring Valley (NY) underwent 6 generations. Vero Beach samples came from field-collected larvae subsequently reared to adulthood. Samples were received as adults in ethanol and silica gel, with the exception of those of Thailand, Japan, and Brazil which were obtained as DNA aliquots. The samples included 24 locations within the USA (Table 1, Fig. 1 and Suppl. material 1). Temporal samples were collected from Connecticut at four locations every year for three years, with the exception of Norwalk, for which only two years were collected.

Table 1.
Download as
CSV
XLSX

Population information and genetic diversity based on 15 microsatellite loci.

ID Location Year N Ho Hs Gis AR
1 Bridgeport, CT, USA 2018 48 0.531 0.664 0.119 5.52
1 Bridgeport, CT, USA 2019 35 0.551 0.657 0.199 5.16
1 Bridgeport, CT, USA 2020 47 0.536 0.642 0.162 5.38
2 Milford, CT, USA 2018 48 0.551 0.667 0.165 5.01
3 New Haven, CT, USA 2018 48 0.591 0.673 0.174 5.07
4 Norwalk, CT, USA 2018 48 0.567 0.678 0.122 5.32
4 Norwalk, CT, USA 2020 46 0.518 0.655 0.164 5.04
5 Stamford, CT, USA 2020 48 0.494 0.637 0.21 4.99
6 Stratford, CT, USA 2018 48 0.573 0.657 0.224 4.95
6 Stratford, CT, USA 2019 18 0.506 0.637 0.128 4.83
6 Stratford, CT, USA 2020 48 0.532 0.646 0.205 4.17
7 West Haven, CT, USA 2018 46 0.564 0.649 0.177 4.92
7 West Haven, CT, USA 2019 39 0.545 0.645 0.132 5.25
7 West Haven, CT, USA 2020 46 0.527 0.662 0.156 5.25
8 Lincoln, DE, USA 2015 25 0.532 0.613 0.204 5.49
9 Washington, DC, USA 2018 47 0.513 0.645 0.132 4.70
10 Riverdale, MD, USA 2015 28 0.494 0.610 0.206 5.20
11 New Bedford, MA, USA 2018 39 0.523 0.633 0.038 5.29
12 Mercer, NJ, USA 2018 48 0.511 0.666 0.191 4.77
13 Tappan, NY, USA * 2018 41 0.531 0.641 0.175 4.85
14 Fire Island, NY, USA * 2018 48 0.537 0.657 0.232 5.18
15 Selden, NY, USA 2019 26 0.556 0.669 0.173 4.40
16 Riverhead, NY, USA 2019 45 0.570 0.684 0.182 4.56
17 Bayview, NY, USA 2019 34 0.530 0.647 0.169 5.46
18 Babylon, NY, USA 2018 46 0.555 0.649 0.168 5.62
19 Spring Valley, NY, USA* 2018 28 0.485 0.597 0.180 4.80
20 Harrisburg, PA, USA 2015 25 0.496 0.655 0.145 5.16
21 Philadelphia, PA, USA 2018 48 0.535 0.625 0.188 4.01
22 Fairfax, VA, USA 2018 46 0.499 0.625 0.243 5.04
- Vero Beach, FL, USA 2018 24 0.658 0.684 0.145 4.95
- San Gabriel, CA, USA 2018 47 0.593 0.673 0.201 6.88
- Manaus, Brazil 2017/18 22 0.459 0.622 0.262 4.88
- Tokyo, Japan 2017/18 42 0.542 0.647 0.162 4.96
- Chanthaburi, Thailand 2016 20 0.651 0.740 0.121 7.23

ID: location identifier in Fig. 1. Locations beyond the focus area are shown in the insert of Fig. 1 and were not assigned an ID; N: number of individuals; Ho: observed heterozygosity; Hs: expected heterozygosity; Gis: Inbreeding Coefficient; AR: estimated by rarefaction (N = 30 genes). *underwent 1–6 generations in laboratory.
Figure 1.

Aedes albopictus collection map. Populations of the northeastern USA (NE) are labeled with numbers, corresponding to their ID in Table 1. Outgroups included in this study, representing known genetic clusters are shown in the world map insert in the bottom right corner.

DNA extraction and microsatellite genotyping

Individual mosquitoes were homogenized with a sterile plastic pestle and DNA was extracted following the Qiagen (Hilden, Germany) protocol for purifying total DNA from insects with the Qiagen DNeasy Blood and Tissue Kit (Hilden, Germany), with an additional RNAse A step. Samples were stored at –20 °C until further use. Mosquitoes from Connecticut, which had previously been homogenized in 1 ml of PBS-G media (phosphate buffered saline, 30% heat-inactivated rabbit serum, 0.5% gelatin), were processed following the manufacturers protocol for electrically homogenized samples.

Mosquitoes were genotyped at 15 microsatellite loci, including locus A9 from Porretta et al. (2006), 11 loci from Beebe et al. (2013), and three new loci developed for this study (Suppl. material 2). The AG10, AG01, and AG07 loci were identified during a screen for candidate trinucleotide microsatellite markers using QDD v.3.1. (Meglécz et al. 2014) on Ae. albopictus genomic data from Palatini et al. (2020). These new loci successfully genotyped across USA populations in a pilot study and were polymorphic across individuals and populations tested (unpublished data). Polymerase chain reactions (PCR) were conducted as loci combinations (Suppl. material 2) in 10 μl reactions using the Type-it Microsatellite PCR Master Mix (Qiagen; Hilden, Germany) and 200 nM of each forward and reverse primer pairs. Thermocycler conditions were: 95 °C × 5’, 5 touch-down cycles reducing the annealing temperature every cycle by 2 °C from 60 °C to 52 °C (95 °C × 30", Tm × 30", 72 °C × 30"), 25× (95 °C × 30", 50 °C × 30", 72 °C × 30"), and 60 °C× 30’ for all loci combos, except for loci set #2 (tri25/AG10), for which we used GoTaq DNA polymerase from Promega (Madison, USA). Primer concentrations were the same for the GoTaq reaction with the thermocycler conditions 95 °C × 2’, 5 touch-down cycles reducing Tm every cycle by 2 °C from 61 °C to 53 °C (95 °C × 45", Tm × 30", 72 °C × 30"), 25× (95 °C × 45", 51 °C × 30", 72 °C × 30"), and 72 °C × 20’.

The resulting products were processed for fragment analysis at the DNA Analysis Facility at Science Hill at Yale University, using GS 500 Liz internal size standard (Applied Biosystems, Waltham MA, USA). Microsatellite alleles were scored using Geneious 11.1.4 (Biomatters Ltd) microsatellite plugin (http://www.geneious.com) using the bins and panels in Suppl. material 3.

Raw allele frequencies are available at VectorBase (www.vectorbase.org), Population Biology Project ID: VBP0000814.

Genetic diversity

Loci were analyzed for within-population deviations from Hardy-Weinberg equilibrium (HWE) using the Weir and Cockerham (1984) exact test as implemented in Genepop v. 4.7.5 (Raymond and Rousset 1995; Rousset 2008). Null allele frequencies and linkage disequilibrium among pairs of loci (LD) were also estimated with this software. HWE and LD tests were run with 10,000 dememorizations, 1000 batches, and 10,000 iterations per batch. Average observed (Ho) and expected (He) heterozygosities, and inbreeding coefficients (Gis) were estimated for each population in GenoDive 3.04 (Meirmans 2020). Allelic richness (AR) was calculated in HP-RARE (Kalinowski 2005), which uses rarefaction to correct for unequal sample sizes (N = 30 genes). Bonferroni correction was applied to the appropriate results to account for multiple testing. A regression analysis in R v. 3.2.2. (R Core Team 2018) was used to evaluate if genetic diversity changed with latitude.

Changes in recent population size were evaluated using Bottleneck v. 1.2.02 (Cornuet and Luikart 1997) under the Infinite Allele Model (IAM) (Maruyama and Fuerst 1985) and the two-phase model (TPM) with a proportion of SMM in the TPM = 0.00 and a variance of the geometric distribution for TPM = 0.36, as recommended by the authors when dealing with microsatellite markers (Cornuet and Luikart 1997). The Wilcoxon sign-rank test (Luikart et al. 1998) was used to determine significance, after Bonferroni multiple test correction.

Effective population size (Ne) was calculated for the temporal collections in CT using NeEstimator (Do et al. 2014) with the Waples (1989) method and three options for computing the standardized variance in allele frequency, F [Fe (Nei and Tajima 1981); Fk (Pollak 1983); and Fs (Jorde and Ryman 2007)]; assuming 3 generations per year. Ne was also estimated from these populations using a single population sample (as opposed to sampling a population multiple times) with the bias-corrected version of the LD method from Waples and Do (2008). Average Ne was estimated using arithmetic and harmonic mean to account for the effect of outliers. Two-sample Ne estimates are known to be robust to overlapping generations and can deal with lower levels of polymorphisms (Luikart et al. 2010), but may be affected by changes in allele frequencies occurring during the time lapsed; while single-sample methods are not affected by gene flow and drift but may be biased by overlapping generations and are unable to distinguish from infinite population sizes when not enough polymorphisms are present (Saarman et al. 2017).

Kinship within collections was assessed in ML-Relate (Kalinowski et al. 2006), which uses maximum likelihood estimates of relatedness to discriminate between four common pedigree relationships: unrelated (U), half-siblings (HS), full-siblings (FS), and parent-offspring (PO). The program tests every population for an excess in heterozygosity relative to the observed allelic diversity.

Population structure

Bayesian clustering analysis was conducted in STRUCTURE v. 2.3 (Pritchard et al. 2000). STRUCTURE identifies genetic clusters and assigns individuals to these clusters with no a priori information of sample location. The most likely number of clusters (K) was determined by conducting 20 independent runs from each K = 1 to 8 for the complete dataset, K = 1 to 11 for Japan + America, K = 1 to 10 for the states at the northeastern invasion front (NY, CT, MA), and K = 1 to 11 for the CT temporal dataset. Each run assumed an admixture model and correlated allele frequencies using a burn-in value of 100,000 iterations followed by 500,000 repetitions. The optimal number of K clusters was determined following the guidelines of Prichard et al. (Pritchard et al. 2000) and the Delta K method (Evanno et al. 2005), as implemented by STRUCTURE HARVESTER (Earl and vonHoldt 2012). Results were plotted with the program CLUMPAK (Kopelman et al. 2015) and DISTRUCT v.1.1 (Rosenberg 2004). Discriminant analysis of Principal Components (DAPC) were conducted on allele frequencies using the ADEGENET package (Jombart 2008) in R v. 3.2.2. (R Core Team 2018) from the same datasets analyzed with STRUCTURE, both using pre-defined populations and with the find.clusters command to identify genetic clusters without a-priori information.

Molecular Analysis of Variance was performed in Genodive 3.04 (Meirmans 2020) with 1000 permutations. Pairwise genetic distances (Fst’) were calculated in the same software. A geographic distance matrix was produced from geographic coordinates in the Geographic distance matrix generator v. 1.2.3. (Ersts 2016). Correlation between genetic and geographic distance (isolation by distance; IBD) was evaluated for all populations in the Northeast, along I-95 interstate corridor from Virginia (VA) to CT, and across the northeastern invasion front (NY, CT, MA), using a Mantel test and 9999 permutations in the Ade4 package (Dray and Dufour 2007) within R (R Core Team 2018).

Results

Genetic diversity

We genotyped a total of 1,342 individual Ae. albopictus mosquitoes from 27 geographic locations at 15 microsatellite loci, for an average of 40 individuals per location (Fig. 1, Table 1). Seventy-nine of the 508 possible population-by-locus comparisons (15.55%) deviate from HWE (p < 0.05) after sequential Bonferroni correction. Putative null alleles were inferred at all loci, except for tri20, with average frequencies across populations between 0.02 – 0.22. Linkage disequilibrium is significant in 37 out of the 3,585 locus-by-locus tests (1.03%) after multiple test correction, consistent with the loci being independent.

There is an average of 13.8 ± 6.46 alleles per locus, ranging from 8 to 31, with a mean allele richness (AR) across populations of AR = 5.13 ± 0.61 (ranging from 4.01 to 7.23; Suppl. material 4). Average observed heterozygosity (Ho) is 0.54 ± 0.42, with a lowest value of 0.46 and a highest of 0.66 observed in Brazil and Florida, respectively (Table 1). The average inbreeding coefficient (Gis) across populations is 0.17 ± 0.04, with a maximum value of 0.26 in Brazil and a minimum of 0.04 in Florida (Table 1). Regression analysis to establish if genetic diversity decays at the invasion front (higher latitudes) indicates that latitude explains a small part of the variation in Ho (adjusted R2 = 0.13, F(1,27), p = 0.03; Suppl. material 9: fig. S1A), with Ho increasing with latitude rather than decreasing. Latitude does not correlate with changes in AR (adjusted R2 = 0.04, F(1,27), p = 0.15; Suppl. material 9: fig. S1B). Genetic diversity at the northern front of the invasion (CT, NY, MA) is no different from that from Japan and Thailand (Ho: t1.02 = - 1.0411, p = 0.4843; AR: t1.01 = - 0.9218, p = 0.5248).

Only four populations have evidence of a recent bottleneck. Bottlenecks were inferred for Fire Island and Spring Valley (NY), Mercer County (NJ), and Norwalk (CT), under both the Infinite Allele Model (IAM) (Maruyama and Fuerst 1985) and the two-phase model (TPM) using the Wilcoxon sign-rank test (Cornuet and Luikart 1997) after a Bonferroni multiple test correction (Suppl. material 5). Among them, Fire Island and Spring Valley had been maintained in the laboratory for six generations prior to genotyping, which may explain the bottleneck signature (Table 1).

Local estimates of effective population size across CT using the two-sample method on temporal collections (see Methods) yield mean values of Ne = 94.97 (harmonic mean) and Ne = 121.21 (arithmetic mean), ranging from 37.70 to 317.10 (Suppl. material 10: fig. S2A). Single-sample estimations based on LD yield a harmonic mean of Ne = 126.84 and an arithmetic mean of Ne = 2,337.50, ranging from 47.40 to 23,830 (Suppl. material 10: fig. S2B); with the highest value estimated for West Haven (2020) as an outlier.

Analysis of kinship determined that, on average, 1.97% of the pairwise relationships within a population involved first degree pairs (Parent-offspring and full sibling; Suppl. material 6). Tappan NY, Spring Valley NY, and Vero Beach FL have the highest percentage of first-degree pairwise relationships (>5%). Removing first-degree relatives from these populations did not have a major impact in the genetic diversity estimates (tHo(4) = 0, p = 1; tGis(4) = 0, p = 1; tAR(3.98) = -0.2964, p = 0.7817), inference of bottlenecks, or the population structure analysis (data not shown).

Population structure

The optimal number of genetic groups inferred from the complete dataset is K = 3, based on Bayesian clustering analysis and the Delta K method (Evanno et al. 2005). The first cluster consists of Florida, California, Brazil, and Thailand while different degrees of admixture between the second and third cluster are observed throughout the rest of the populations analyzed, including Japan and the northeastern USA (Fig. 2A). This grouping is consistent with the DAPC using predefined populations, except that in the DAPC plot Florida is placed within the cluster that includes the northeastern USA (Fig. 2B). No clear genetic structure was detected within the genetic cluster that included Japan and eastern North America, despite a suggested K = 3 using the Delta K method (Suppl. material 11). Incipient population structure is suggested by the clustering analysis of the populations at the northeastern invasion front, with Fire Island and Bayview (NY) showing certain differentiation at K = 3 (Suppl. material 12).

Figure 2.

Population structure on the complete Aedes albopictus dataset based on 15 microsatellite markers A STRUCTURE plot with each individual represented by a vertical bar. The height of each bar is the probability of assignment to each of K = 3 genetic clusters (indicated by different colors) B discriminant analysis of principal components (DAPC).

Analysis of Molecular Variance (AMOVA) on the complete dataset indicates that most of the variation can be explained at the individual level, with a lower contribution from the population level (Table 2).

Table 2.
Download as
CSV
XLSX

Analysis of Molecular Variance on all populations genotyped for 15 microsatellite loci.

Source of Variation Nested in % var F-stat F-value Std.Dev. P-value
Within Individual – 0.792 F_it 0.208 0.049 –
Among Individual Population 0.160 F_is 0.168 0.050 0.001
Among Population – 0.047 F_st 0.047 0.004 0.001

We then tested for isolation by distance (IBD) throughout the northeastern USA (Virginia, District of Columbia, New Jersey, NY, CT, and MA) to determine whether genetic distance (Fst) was correlated with geographic distance (Km) and found no correlation (Mantel statistic = -0.0406, p = 0.4368; Suppl. materials 7, 13). Likewise, there was no IBD in populations located along the I-95 corridor from Virginia to CT (Mantel statistic = 0.088, p = 0.295; Suppl. material 7 and Suppl. material 13), or at the northeastern invasion front: CT, NY, MA (Mantel statistic = 0.382, p = 0.072; Suppl. material 7 and Fig. 3A). However, strong IBD was detected when only NY and CT were analyzed (Mantel statistic = 0.727, p = 0.000; Suppl. material 7 and Fig. 3B).

Figure 3.

Geographic genetic differentiation (IBD: isolation by distance) across A New York, Connecticut, and Massachusetts; and B New York and Connecticut. Genetic distance is given as the linearized Fst [Fst/(1/Fst)] and geographic distance is provided in kilometers (Km). Statistical significance was evaluated using a Mantel test, yielding a significant positive slope only when Massachusetts is excluded (p = 0.072 and p < 0.000 in A and B, respectively).

Temporal stability

Bayesian clustering analysis and DAPC across all Connecticut populations indicate weak population structure in CT (Suppl. material 14). Analysis of the temporal series indicates that these population clusters prevail over multiple years, suggesting the development of local populations (Fig. 4). In contrast, there is no support for temporal structure by year of collection (Fig. 4). This result agrees with the AMOVA, with variation mostly explained at the individual and population level rather than by year of collection (Table 3; AMOVATime_Points p = 0.901). When DAPC was used to infer genetic clusters without population priors, three genetic clusters were inferred (Suppl. material 15). However, these clusters include individuals from all collection points and years (Suppl. material 8), with very few individuals assigned to a third cluster, in agreement with the incipient differentiation suggested by the Bayesian clustering analyses.

Table 3.
Download as
CSV
XLSX

Analysis of Molecular Variance on temporal samples from Connecticut genotyped for 15 microsatellite loci.

Source of Variation Nested in %var F-stat F-value Std.Dev. P-value
Within Individual – 0.801 F_it 0.199 0.044 –
Among Individual Population 0.183 F_is 0.186 0.044 0.001
Among Population Series_A 0.018 F_sc 0.018 0.004 0.001
Among Time points – -0.002 F_ct -0.002 0.002 0.901
Figure 4.

Population structure on Aedes albopictus samples from the Connecticut temporal series based on 15 microsatellite markers A STRUCTURE plot with each individual represented by a vertical bar. The height of each bar is the probability of assignment to each of K = 3 genetic clusters (indicated by different colors) B discriminant analysis of principal components (DAPC). Partially overlapping genetic clusters can be distinguished, grouping temporal collections from the same location.

Discussion

We find that Ae. albopictus from the northeastern USA are related to Ae. albopictus from Japan and harbor high genetic diversity with limited geographic structure. This suggests regional gene flow and a northward invasion driven by a combination of multiple local and long-distance dispersal events that has led to the establishment of northern populations overwintering locally.

Discarded tires are preferred breeding sites for container-inhabiting Aedes mosquitoes (Yee 2008) and likely explain how this species entered the country. The USA began importing used tires from Japan in 1968, and by the mid-1970’s most used tires were imported from countries where Ae. albopictus was native, mostly from Japan and Taiwan (Sprenger and Reiter 1987). Our results agree with previous work showing that eastern USA populations most likely originated from northern (temperate) East Asia, based on historical records, phenotypic traits (photoperiod sensitivity and cold-hardiness), and genetic markers (Hawley et al. 1987; Kambhampati et al. 1991; Kotsakiozi et al. 2017). We also find that the population in southern California is genetically distinct from those occupying eastern USA, consistent with reports of an introduction of Chinese origin in 2001 and 2011 (Linthicum et al. 2003, Zhong et al. 2013).

Shortly after its initial detection in Texas in 1985 (Moore 1999, Hahn et al 2016), Ae. albopictus rapidly spread throughout much of eastern USA. Currently the states of CT, MA, and NY represent the northern limit of the distribution. Classic invasive population genetics predicts that populations at the invasion front would have reduced genetic diversity, consequence of founder effects during the colonization process (Nei et al. 1975; Sakai et al. 2001). We find high genetic diversity (Ho) at the Ae. albopictus northern invasion front, equivalent to that in the native range: Japan and Thailand. Furthermore, evidence of recent bottlenecks (founder effects) was restricted to the two collections from New York that spent 6 generations in the laboratory (Spring Valley and Fire Island), and Mercer County (NJ), and Norwalk (CT). Since bottlenecks are common after laboratory colonization (Gloria-Soria et al. 2019), the bottlenecks detected in Spring Valley and Fire Island are likely the result of the colonization process. A growing number of studies have now demonstrated that the genetic diversity patterns following an invasion event are complex and depend on the size of the propagule (number of individuals invading), frequency of introductions, number of sources, admixture events, or a combination of these (Lockwood et al 2005; Dlugosch and Parker 2008; Facon et al. 2008; Handley et al. 2011; Bock et al. 2015; Jaspers et al. 2021). Different invasion scenarios may result in lower, equal, or higher genetic diversity metrics in the non-native range relative to the native range (Jaspers et al. 2021). High Ho values at the invasive range of Ae. albopictus have also been reported by others using allozymes (Black et al. 1988), microsatellites (Manni et al. 2017), and genome-wide single nucleotide polymorphisms [SNPs] (Kotsakiozi et al. 2017). The observed genetic diversity in the northeastern USA could be explained by expanding propagules that are subjected to drift and then merge (admixture), or by constant input of alleles that restore the original diversity levels and could possibly exceed them (Lockwood et al. 2005; Facon et al. 2008). In Ae. japonicus, another Asian container-breeding mosquito that invaded the USA, merging of two genetic groups was reported in Pennsylvania between 1999/2000 and 2004/2005 and resulted in the loss of the original introduction bottleneck signature and high levels of genetic diversity (Fonseca et al. 2010).

The heterozygosity values observed in Ae. albopictus in the Northeast USA are equivalent to those observed in Ae. aegypti in the USA (t17.7 = 1.027, p = 0.318; Gloria-Soria et al. 2016). Despite this similarity, estimates of inbreeding are an order of magnitude larger in Ae. albopictus than in Ae. aegypti (Gloria-Soria et al. 2016). High Ae. albopictus inbreeding values have been previously reported in the USA using allozymes (Black et al. 1988) and in populations outside Ae. albopictus native range with microsatellites (Beebe et al. 2013), and may reflect the local breeding structure of this container mosquito (Black et al. 1988). Alternatively, the increase in homozygosity relative to the expected Hardy-Weinberg equilibrium diagnostic of inbreeding may also be the result of a Wahlund effect or the presence of null alleles, and distinguishing among those mechanisms is not trivial (Barros et al. 2020). We detected putative null alleles at low frequencies (0.02 – 0.22) at all but one of the 15 loci used in this study. Microsatellite null alleles are frequent in insects (Chapuis and Estoup 2007), and in Ae. albopictus (Beebe et al. 2013; Manni et al. 2017). Studies have shown that at low frequencies (< 0.20), the presence of null alleles does not affect analyses of genetic diversity and population structure (Dakin and Avise 2004; Chapuis and Estoup 2007; Wei et al. 2019). In 2017 De Meeûs (2018) proposed a statistical test to differentiate the null alleles from a Wahlund effect, based on correlations among F-statistics. Null alleles are expected to increase both Fis and Fst, creating a strong positive correlation, while a Wahlund effect will move the values in the opposite direction and generate weak or no correlation. We find no correlation between Gis and Gst (equivalent to Fis and Fst), indicating that either a Wahlund effect or inbreeding (or both), are most likely responsible for the observed heterozygote deficits rather than null alleles (R2 = 0.024, F(1-13) = 0.328, p = 0.577). A Wahlund effect arises when genotype proportions are calculated from samples that include individuals belonging to genetically differentiated groups in time or space, for example, subpopulations or cohorts (De Meeûs 2018). It is thus possible that the diversity pattern we observe is the result of substructure within Ae. albopictus collections that goes undetected due to the scale of this work. The latter will be consistent with the small neighborhood size estimated for Ae. albopictus in Connecticut (Ne ~ 100), which is overall lower than those estimated from wild Ae. aegypti using 12 microsatellite markers by Saarman et al. (2017).

At the regional scale, Ae. albopictus in the northeastern USA is genetically homogeneous. This lack of population structure is congruent with the findings of Kotsakiozi et al. (2017) using ~58,000 genome-wide SNP and likely reflects the demographic features of these species, rather than a lack of marker resolution. One possibility is that being a relatively new invasion there has not been enough time for detectable genetic differentiation to arise. However, fine scale structure is evident in Ae. aegypti from California just two years after breeding populations were first detected (Gloria-Soria et al. 2014; Pless et al. 2017). The absence of population structure in Ae. albopictus may be better explained by the invasion history of Ae. albopictus, spreading faster than Ae. aegypti in North America due to its biology and propagule size, and the high connectivity within the region. Consistent with this hypothesis, we detect isolation by distance along CT and NY that does not extend to MA or the rest of the East Coast. This is probably a consequence of the proximity of CT and NY, with gene-flow predominantly occurring via neighboring populations through natural and human-aided dispersal (Handley et al. 2011; Medley et al. 2015). Geographic differentiation within this area is observed and suggests that these populations may already be established and had sufficient time to differentiate.

In Connecticut, Ae. albopictus has been recorded every year since 2010 (Armstrong et al. 2017) but it has not been determined whether these populations are present year-round or are reintroduced annually. Unlike its congener Ae. aegypti, Ae. albopictus is capable of diapausing at the egg stage (Armbruster 2016) and overwintering has been reported in CT after mild winters (Armstrong et al. 2017). If CT was recolonized from the south every year, we would expect that collections from one year will be more similar to each other than between years. We did not find evidence of temporal structure in these collections but rather a weak spatial signature across years, consistent with overwintering. However, at this point we cannot exclude the possibility that these populations are recolonized by a large influx of individuals from the same sources every year.

Conclusions

The overall absence of bottlenecks, lack of genetic structure, patterns of isolation by distance, and temporal stability at the northeastern invasive front suggest that Ae. albopictus populations in the northeastern USA may already be established as overwintering populations. Furthermore, the high levels of genetic diversity, signatures of inbreeding and small neighborhood sizes suggest that Ae. albopictus populations in the northeast USA experience high propagule pressure, probably as the result of multiple, diverse, and frequent invasion sources from southeastern USA populations and possibly from abroad. We suggest that Ae. albopictus in eastern USA behave as a metapopulation, in which genetic variation is consistently introduced to the area via human-aided dispersal, and where local genetic drift and selection lead to differentiated small breeding units interconnected across space and time, with admixture through secondary contact further increasing variability.

Acknowledgements

The authors would like to thank the following collaborators that contributed with samples for this project: M. Hutchinson (Pennsylvania Department of Agriculture); A. Lima, J. Smith, Y. Tan, and the Fairfax County Health Department, Fairfax, VA; M. Helwig, C.N. Boyer, and the Pennsylvania Department of Environmental Protection, Vector Management Program; K. Itokawa (Department of Medical Entomology, National Institute of Infectious Diseases, Tokyo, Japan) and R. Uraki (National Center for Global Health and Medicine); C.R. Courtney and the DFS DC Public Health Laboratory; L.C. Harrington (Cornell U.); M. Doyle and the San Gabriel Mosquito and Vector Control District (California); M.P. Santoriello and C.L. Romano (Suffolk County Department of Health Services); and numerous seasonal staff at the different institutions for assistance with mosquito surveillance, identification and processing.

This publication was funded the Cooperative Agreement U01CK000509, awarded by the Center for Disease Control and Prevention. Its content is solely the responsibility of the authors and do not necessarily represent the official views of the Center for Disease Control and Prevention or the Department of Health and Human Services. The funding agency did not participate in the design of the study, collection, analysis, or interpretation of data. The study was partly funded by the National Science Foundation-Coupled Natural Human Systems award (DEB 1824807). CA was supported by the National Institutes of Health under award number R01AI132409 (PI: Caccone).

References

  • Andreadis TG (2009) Failure of Aedes albopictus to overwinter following introduction and seasonal establishment at a tire recycling plant in the northeastern USA. Journal of the American Mosquito Control Association 25(1): 25–31. https://doi.org/10.2987/08-5813.1
  • Andreadis TG, Thomas MC, Shepard JJ (2005) Identification guide to the mosquitoes of Connecticut. The Connecticut Agricultural Experiment Station, New Haven, CT.
  • Armbruster PA (2016) Photoperiodic diapause and the establishment of Aedes albopictus (Diptera: Culicidae) in North America. Journal of Medical Entomology 53(5): 1013–1023. https://doi.org/10.1093/jme/tjw037
  • Armstrong PM, Andreadis TG, Shepard JJ, Thomas MC (2017) Northern range expansion of the Asian tiger mosquito (Aedes albopictus): Analysis of mosquito data from Connecticut, USA. PLoS Neglected Tropical Diseases 11(5): e0005623. https://doi.org/10.1371/journal.pntd.0005623
  • Barros J, Winkler FM, Velasco LA (2020) Assessing the genetic diversity in Argopecten nucleus (Bivalvia: Pectinidae), a functional hermaphrodite species with extremely low population density and self‐fertilization: Effect of null alleles. Ecology and Evolution 10(9): 3919–3931. https://doi.org/10.1002/ece3.6080
  • Beebe NW, Ambrose L, Hill LA, Davis JB, Hapgood G, Cooper RD, Russell RC, Ritchie SA, Reimer LJ, Lobo NF, Syafruddin D, van den Hurk AF (2013) Tracing the tiger: Population genetics provides valuable insights into the Aedes (Stegomyia) albopictus invasion of the Australasian Region. PLoS Neglected Tropical Diseases 7(8): e2361. https://doi.org/10.1371/journal.pntd.0002361
  • Black IV WC, Ferrari JA, Rai KS, Sprenger D (1988) Breeding structure of a colonizing species: Aedes albopictus (Skuse) in the United States. Heredity 60(2): 173–181. https://doi.org/10.1038/hdy.1988.29
  • Bock DG, Caseys C, Cousens RD, Hahn MA, Heredia SM, Hübner S, Turner KG, Whitney KD, Rieseberg LH (2015) What we still don’t know about invasion genetics. Molecular Ecology 24(9): 2277–2297. https://doi.org/10.1111/mec.13032
  • Chapuis MP, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Molecular Biology and Evolution 24(3): 621–631. https://doi.org/10.1093/molbev/msl191
  • Cornuet JM, Luikart G (1997) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144(4): 2001–2014. https://doi.org/10.1093/genetics/144.4.2001
  • Delatte H, Desvars A, Bouétard A, Bord S, Gimonneau G, Vourc’h G, Fontenille D (2010) Blood-feeding behavior of Aedes albopictus, a vector of Chikungunya on La Réunion. Vector Borne and Zoonotic Diseases (Larchmont, N.Y. ) 10(3): 249–258. https://doi.org/10.1089/vbz.2009.0026
  • Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014) NeEstimator v2: Re- implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Molecular Ecology Resources 14(1): 209–214. https://doi.org/10.1111/1755-0998.12157
  • Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4(2): 359–361. https://doi.org/10.1007/s12686-011-9548-7
  • Facon B, Pointier JP, Jarne P, Sarda V, David P (2008) High genetic variance in life-history strategies within invasive populations by way of multiple introductions. Current Biology 18(5): 363–367. https://doi.org/10.1016/j.cub.2008.01.063
  • Fonseca DM, Widdel AK, Hutchinson M, Spichiger SE, Kramer LD (2010) Fine‐scale spatial and temporal population genetics of Aedes japonicus, a new US mosquito, reveal multiple introductions. Molecular Ecology 19(8): 1559–1572. https://doi.org/10.1111/j.1365-294X.2010.04576.x
  • Gloria-Soria A, Brown JE, Kramer V, Hardstone Yoshimizu M, Powell JR (2014) Origin of the dengue fever mosquito, Aedes aegypti, in California. PLoS Neglected Tropical Diseases 8(7): e3029. https://doi.org/10.1371/journal.pntd.0003029
  • Gloria‐Soria A, Ayala D, Bheecarry A, Calderon‐Arguedas O, Chadee DD, Chiappero M, Coetzee M, Elahee KB, Fernandez‐Salas I, Kamal HA, Kamgang B, Khater EIM, Kramer LD, Kramer V, Lopez-Solis A, Lutomiah J, Martins Jr A, Micieli MV, Paupy C, Ponlawat A, Rahola N, Rasheed SB, Richardson JB, Saleh AA, Sanchez-Casas RM, Seixas G, Sousa CA, Tabachnick WJ, Troyo A, Powell JR (2016) Global genetic diversity of Aedes aegypti. Molecular Ecology 25(21): 5377–5395. https://doi.org/10.1111/mec.13866
  • Gloria-Soria A, Soghigian J, Kellner D, Powell JR (2019) Genetic diversity of laboratory strains and implications for research: The case of Aedes aegypti. PLoS Neglected Tropical Diseases 13(12): е0007930. https://doi.org/10.1371/journal.pntd.0007930
  • Gloria-Soria A, Payne AF, Bialosuknia SM, Stout J, Mathia N, Eastwood G, Ciota AT, Kramer LD, Armstrong PM (2021) Vector competence of Aedes albopictus populations from the Northeastern United States for chikungunya, dengue, and Zika viruses. The American Journal of Tropical Medicine and Hygiene 104(3): 1123. https://doi.org/10.4269/ajtmh.20-0874
  • Grandadam M, Caro V, Plumet S, Thiberge J-M, Souarés Y, Failloux A-B, Tolou HJ, Budelot M, Cosserat D, Leparc-Goffart I, Després P (2011) Chikungunya virus, Southeastern France. Emerging Infectious Diseases 17(5): 910–913. https://doi.org/10.3201/eid1705.101873
  • Hahn MB, Eisen RJ, Eisen L, Boegler KA, Moore CG, McAllister J, Savage HM, Mutebi JP (2016) Reported distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus in the United States, 1995–2016 (Diptera: Culicidae). Journal of Medical Entomology 53(5): 1169–1175. https://doi.org/10.1093/jme/tjw072
  • Hahn MB, Eisen L, McAllister J, Savage HM, Mutebi JP, Eisen RJ (2017) Updated reported distribution of Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the United States, 1995–2016. Journal of Medical Entomology 54(5): 1420–1424. https://doi.org/10.1093/jme/tjx088
  • Handley LJL, Estoup A, Evans DM, Thomas CE, Lombaert E, Facon B, Aebi A, Roy HE (2011) Ecological genetics of invasive alien species. BioControl 56(4): 409–428. https://doi.org/10.1007/s10526-011-9386-2
  • Hawley WA, Reiter P, Copeland RS, Pumpuni CB, Craig Jr GB (1987) Aedes albopictus in North America: Probable introduction in used tires from northern Asia. Science 236(4805): 1114–1116. https://doi.org/10.1126/science.3576225
  • Jaspers C, Ehrlich M, Pujolar JM, Künzel S, Bayer T, Limborg MT, Lombard F, Browne WE, Stefanova K, Reusch TB (2021) Invasion genomics uncover contrasting scenarios of genetic diversity in a widespread marine invader. Proceedings of the National Academy of Sciences of the United States of America 118(51): e2116211118. https://doi.org/10.1073/pnas.2116211118
  • Kache PA, Eastwood G, Collins-Palmer K, Katz M, Falco RC, Bajwa WI, Armstrong PM, Andreadis TG, Diuk-Wasser MA (2020) Environmental determinants of Aedes albopictus abundance at a northern limit of its range in the United States. The American Journal of Tropical Medicine and Hygiene 102(2): 436–447. https://doi.org/10.4269/ajtmh.19-0244
  • Kambhampati S, Black WC IV, Rai KS (1991) Geographic origin of the US and Brazilian Aedes albopictus inferred from allozyme analysis. Heredity 67(1): 85–94. https://doi.org/10.1038/hdy.1991.67
  • Kobayashi M, Nihei N, Kurihara T (2002) Analysis of northern distribution of Aedes albopictus (Diptera: Culicidae) in Japan by geographical information system. Journal of Medical Entomology 39(1): 4–11. https://doi.org/10.1603/0022-2585-39.1.4
  • Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Molecular Ecology Resources 15(5): 1179–1191. https://doi.org/10.1111/1755-0998.12387
  • Kotsakiozi P, Richardson JB, Pichler V, Favia G, Martins AJ, Urbanelli S, Armbruster PA, Caccone A (2017) Population genomics of the Asian tiger mosquito, Aedes albopictus: Insights into the recent worldwide invasion. Ecology and Evolution 7(23): 10143–10157. https://doi.org/10.1002/ece3.3514
  • Kraemer MU, Sinka ME, Duda KA, Mylne AQ, Shearer FM, Barker CM, Moore CG, Carvalho RG, Coelho GE, Van Bortel W, Hendrickx G, Schaffner F, Elyazar IRF, Teng H-J, Brady OJ, Messina JP, Pigott DM, Scott TW, Smith DL, Wint GRW, Golding N, Hay SI (2015) The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. eLife 4: e08347. https://doi.org/10.7554/eLife.08347
  • Kulasekera VL, Kramer L, Nasci RS, Mostashari F, Cherry B, Trock SC, Glaser C, Miller JR (2001) West Nile virus infection in mosquitoes, birds, horses, and humans, Staten Island, New York, 2000. Emerging Infectious Diseases 7(4): 722–725. https://doi.org/10.3201/eid0704.017421
  • Linthicum KJ, Kramer VL, Madon MB, Fujioka K (2003) Introduction and potential establishment of Aedes albopictus in California in 2001. Journal of the American Mosquito Control Association 19(4): 301–308.
  • Luikart G, Allendorf FW, Cornuet JM, Sherwin WB (1998) Distortion of allele frequency distributions provides a test for recent population bottlenecks. The Journal of Heredity 89(3): 238–247. https://doi.org/10.1093/jhered/89.3.238
  • Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf FW (2010) Estimation of census and effective population sizes: The increasing usefulness of DNA-based approaches. Conservation Genetics 11(2): 355–373. https://doi.org/10.1007/s10592-010-0050-7
  • Manni M, Guglielmino CR, Scolari F, Vega-Rúa A, Failloux AB, Somboon P, Lisa A, Savini G, Bonizzoni M, Gomulski LM, Malacrida AR, Gasperi G (2017) Genetic evidence for a worldwide chaotic dispersion pattern of the arbovirus vector, Aedes albopictus. PLoS Neglected Tropical Diseases 11(1): e0005332. https://doi.org/10.1371/journal.pntd.0005332
  • Maruyama T, Fuerst PA (1985) Population bottlenecks and non-equilibrium models in population genetics. II. Number of alleles in a small population that was formed by a recent bottleneck. Genetics 111(3): 675–689. https://doi.org/10.1093/genetics/111.3.675
  • Medley KA, Jenkins DG, Hoffman EA (2015) Human‐aided and natural dispersal drive gene flow across the range of an invasive mosquito. Molecular Ecology 24(2): 284–295. https://doi.org/10.1111/mec.12925
  • Meglécz E, Pech N, Gilles A, Dubut V, Hingamp P, Trilles A, Grenier R, Martin JF (2014) QDD version 3.1: A user‐friendly computer program for microsatellite selection and primer design revisited: Experimental validation of variables determining genotyping success rate. Molecular Ecology Resources 14(6): 1302–1313. https://doi.org/10.1111/1755-0998.12271
  • Meirmans PG (2020) Genodive version 3.0: Easy‐to‐use software for the analysis of genetic data of diploids and polyploids. Molecular Ecology Resources 20(4): 1126–1131. https://doi.org/10.1111/1755-0998.13145
  • Metselaar D, Grainger CR, Oei KG, Reynolds DG, Pudney M, Leake CJ, Tukei PM, d’Offay RM, Simpson DIH (1980) An outbreak of type 2 dengue fever in the Seychelles, probably transmitted by Aedes albopictus (Skuse). Bulletin of the World Health Organization 58(6): 937.
  • Moore CG (1999) Aedes albopictus in the United States: Current status and prospects for further spread. Journal of the American Mosquito Control Association 15(2): 221–227.
  • Nawrocki SJ, Hawley WA (1987) Estimation of the northern limits of distribution of Aedes albopictus in North America. Journal of the American Mosquito Control Association 3(2): 314–317.
  • Niebylski ML, Savage HM, Nasci RS (1994) Blood hosts of Aedes albopictus in the United States. Journal of the American Mosquito Control Association 0(3): 447–450.
  • Palatini U, Masri RA, Cosme LV, Koren S, Thibaud-Nissen F, Biedler JK, Krsticevic F, Johnston JS, Halbach R, Crawford JE, Antoshechkin I, Failloux A-B, Pischedda E, Marconcini M, Ghurye J, Rhie A, Sharma A, Karagodin DA, Jenrette J, Gamez S, Miesen P, Masterson P, Caccone A, Sharakhova MV, Tu Z, Papathanos PA, Van Rij RP, Akbari OS, Powell J, Phillippy AM, Bonizzoni M (2020) Improved reference genome of the arboviral vector Aedes albopictus. Genome Biology 21(1): 1–29. https://doi.org/10.1186/s13059-020-02141-w
  • Parker AT, McGill K, Allan BF (2020) Container type affects mosquito (Diptera: Culicidae) oviposition choice. Journal of Medical Entomology 57(5): 1459–1467. https://doi.org/10.1093/jme/tjaa045
  • Paupy C, Delatte H, Bagny L, Corbel V, Fontenille D (2009) Aedes albopictus, an arbovirus vector: From the darkness to the light. Microbes and Infection 11(14–15): 1177–1185. https://doi.org/10.1016/j.micinf.2009.05.005
  • Paupy C, Kassa Kassa F, Caron M, Nkoghé D, Leroy EM (2012) A chikungunya outbreak associated with the vector Aedes albopictus in remote villages of Gabon. Vector Borne and Zoonotic Diseases (Larchmont, N.Y. ) 12(2): 167–169. https://doi.org/10.1089/vbz.2011.0736
  • Pless E, Gloria-Soria A, Evans BR, Kramer V, Bolling BG, Tabachnick WJ, Powell JR (2017) Multiple introductions of the dengue vector, Aedes aegypti, into California. PLoS Neglected Tropical Diseases 11(8): e0005718. https://doi.org/10.1371/journal.pntd.0005718
  • Porretta D, Gargani M, Bellini R, Calvitti M, Urbanelli S (2006) Isolation of microsatellite markers in the tiger mosquito Aedes albopictus (Skuse). Molecular Ecology Notes 6(3): 880–881. https://doi.org/10.1111/j.1471-8286.2006.01384.x
  • R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
  • Rochlin I, Ninivaggi DV, Hutchinson ML, Farajollahi A (2013) Climate change and range expansion of the Asian tiger mosquito (Aedes albopictus) in Northeastern USA: Implications for public health practitioners. PLoS ONE 8(4): e60874. https://doi.org/10.1371/journal.pone.0060874
  • Saarman NP, Gloria‐Soria A, Anderson EC, Evans BR, Pless E, Cosme LV, Gonzalez‐Acosta C, Kamgang B, Wesson DM, Powell JR (2017) Effective population sizes of a major vector of human diseases, Aedes aegypti. Evolutionary Applications 10(10): 1031–1039. https://doi.org/10.1111/eva.12508
  • Sakai AK, Allendorf FW, Holt JS, Lodge DM, Molofsky J, With KA, Baughman S, Cabin RJ, Cohen JE, Ellstrand NC, McCauley DE, O’Neil P, Parker IM, Thompson JN, Weller SG (2001) The population biology of invasive species. Annual Review of Ecology and Systematics 32(1): 305–332. https://doi.org/10.1146/annurev.ecolsys.32.081501.114037
  • Sprenger P, Reiter D (1987) The used tire trade: A mechanism for the worldwide dispersal of container breeding mosquitoes. Journal of the American Mosquito Control Association 3: 494.
  • Sprenger D, Wuithiranyagool T (1986) The discovery and distribution of Aedes albopictus in Harris County, Texas. Journal of the American Mosquito Control Association 2(2): 217–219.
  • Wei Y, Wang J, Song Z, He Y, Zheng Z, Fan P, Yang D, Zhou G, Zhong D, Zheng X (2019) Patterns of spatial genetic structures in Aedes albopictus (Diptera: Culicidae) populations in China. Parasites & Vectors 12(1): 1–15. https://doi.org/10.1186/s13071-019-3801-4
  • Zhong D, Lo E, Hu R, Metzger ME, Cummings R, Bonizzoni M, Fujioka KK, Sorvillo TE, Kluh S, Healy SP, Fredregill C, Kramer VL, Chen X, Yan G (2013) Genetic analysis of invasive Aedes albopictus populations in Los Angeles County, California and its potential public health impact. PLoS ONE 8(7): e68586. https://doi.org/10.1371/journal.pone.0068586

Supplementary materials

Supplementary material 1 

Detailed collection information

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (10.87 kb)
Supplementary material 2 

Aedes albopictus microsatellite primers used in this study

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (10.26 kb)
Supplementary material 3 

Microsatellite bins used to call alleles in Geneious v. 11.1.5 (Biomatters LTD)

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (16.75 kb)
Supplementary material 4 

Allele numbers and allelic richness at 15 microsatellite loci used in this study

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

Explanation note: Allele numbers and allelic richness at 15 microsatellite loci used in this study obtained using rarefaction to correct for unequal sample sizes (N = 30 genes) in HP-RARE (Kalinowski 2005).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (13.84 kb)
Supplementary material 5 

Probability of a recent bottleneck at each Aedes albopictus location, under the infinite allele model (IAM) and the two-phase model (TPM) with variance of 0.36

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

Explanation note: Probability of a recent bottleneck at each Aedes albopictus location, under the infinite allele model (IAM) and the two-phase model (TPM) with variance of 0.36; as estimated using the software Bottleneck v. 1.2.02 (Cornuet and Luikart, 1997). The Wilcoxon sign-rank test (Luikart et al. 1998) was used to determine significance. Values in bold indicate significant differences after Bonferroni multiple test correction.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (15.54 kb)
Supplementary material 6 

Kinship analysis

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

Explanation note: Summary of pedigree relationships within Aedes albopictus collections. Unrelated (U), half-siblings (HS), full-siblings (FS), and parent-offspring (PO), as estimated by ML-Relate (Kalinowski et al. 2006). Values in bold highlight collections with the frequency of first-degree relatives above 5%.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (11.98 kb)
Supplementary material 7 

Isolation by distance analyses (IBD)

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

Explanation note: Matrices of geographic distance in meters and genetic distance as Fst. Results from Mantel tests for Aedes albopictus in the northeastern USA; the I-95 interstate corridor from Virginia (VA) to CT; the northeastern expansion front (New York, Connecticut, Massachusetts); and through Connecticut and New York.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (19.41 kb)
Supplementary material 8 

Genetic clusters inferred from all Connecticut collections using discriminant analysis of principal components in the ADEGENET package

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: table (excel file)

Explanation note: Genetic clusters inferred from all Connecticut collections using discriminant analysis of principal components in the ADEGENET package (Jombart 2008) in R v. 3.2.2. (R Core Team 2018).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (9.26 kb)
Supplementary material 9 

Latitude of each northeastern USA Aedes albopictus location

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: Latitude of each northeastern USA Aedes albopictus location plotted against A its observed heterozygosity (Ho) and B allelic richness estimated by rarefaction (N = 30). Linear regression in R v. 3.2.2. (R Core Team 2018) indicates a correlation between latitude and Ho (adjusted R2 = 0.13, F(1,27), p = 0.03) but not between latitude and allelic richness (adjusted R2 = 0.04, F(1,27), p = 0.15).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (41.29 kb)
Supplementary material 10 

Estimates of effective population size based of Connecticut populations obtained with NeEstimator (Do et al. 2014)

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: Estimates of effective population size based of Connecticut populations obtained with NeEstimator (Do et al. 2014) using A population pairs using the two-sample Waples (1989) method and three options for computing the standardized variance in allele frequency, F [Fe (Nei and Tajima 1981); Fk (Pollak 1983); and Fs (Jorde and Ryman 2007)]; and B a single population sample using the bias-corrected version of the linkage disequilibrium method Waples and Do (2008). Mean effective population size estimates (Ne), 95% confidence intervals (CI) are displayed by locality. The average Ne across all estimates is displayed with a dashed (harmonic mean) and dotted (arithmetic mean) horizontal lines. Note that the arithmetic mean of B is missing as it falls outside the plotted area (Ne = 2,337.5).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (115.71 kb)
Supplementary material 11 

Population structure of Aedes albopictus from the United States and Japan based on 15 microsatellite markers

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: A STRUCTURE plot with each individual represented by a vertical bar. The height of each bar is the probability of assignment to each of K = 3 genetic clusters (indicated by different colors). B Discriminant analysis of principal components (DAPC).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (3.18 MB)
Supplementary material 12 

Population structure of Aedes albopictus at the United States northeastern invasion front (New York, Connecticut, Massachusetts) based on 15 microsatellite markers

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: Population structure of Aedes albopictus at the United States northeastern invasion front (New York, Connecticut, Massachusetts) based on 15 microsatellite markers. A Discriminant analysis of principal components (DAPC) and B STRUCTURE plot with each individual represented by a vertical bar. The height of each bar is the probability of assignment to each of K = 3 and K = 6 genetic clusters (indicated by different colors).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (2.15 MB)
Supplementary material 13 

Geographic genetic differentiation (IBD: isolation by distance) across the Northeast USA

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: Genetic distance is given as the linearized Fst [Fst/(1/Fst)] and geographic distance is provided in kilometers (Km).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (48.07 kb)
Supplementary material 14 

Population structure on Aedes albopictus samples from all Connecticut samples (no temporal series) based on 15 microsatellite markers

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: A STRUCTURE plot with each individual represented by a vertical bar. The height of each bar is the probability of assignment to each of K = 3 genetic clusters (indicated by different colors). B Discriminant analysis of principal components (DAPC).

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (871.80 kb)
Supplementary material 15 

Inferred genetic clusters from Aedes albopictus of the Connecticut temporal series

Andrea Gloria-Soria, Talya Shragai, Alexander T. Ciota, Todd B. Duval, Barry W. Alto, Ademir J. Martins, Kathleen M. Westby, Kim A. Medley, Isik Unlu, Scott R. Campbell, Malgorzata Kawalkowski, Yoshio Tsuda, Yukiko Higa, Nicholas Indelicato, Paul T. Leisnham, Adalgisa Caccone, Philip M. Armstrong

Data type: figure (pdf file)

Explanation note: Inferred genetic clusters from Aedes albopictus of the Connecticut temporal series using Discriminant Analysis of Principal Components in ADEGENET (Jombart 2008)

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (43.50 kb)
login to comment