Introduction {#S1}
============

*Obolodiplosis robiniae* ([@B16]) (Diptera: Cecidomyiidae) is a North American species of gall midge that has recently been extensively introduced throughout Asia and Europe ([@B6]) and is continuously expanding its range ([@B8]; [@B57]; [@B3]; [@B22]). It is specifically associated with host plants from the genus *Robinia* (Fabaceae) ([@B57]). Its main host is *Robinia pseudoacacia* although it is occasionally found on *R. pseudoacacia* cv. 'Frisia' ([@B3]). The gall midge causes leaf rolling and premature leaf shedding, resulting in the deterioration of the host and increased susceptibility to other pests, including wood borers such as longhorn beetles ([@B64]).

*Obolodiplosis robiniae* was first recorded in China (Qinhuangdao City, Hebei Province) in 2004 ([@B64]) and has since spread extensively. Its primary host (*R. pseudoacacia*) has been planted extensively across China, and *O. robiniae* is now found in most of these areas ([@B50]). Chinese *O. robiniae* populations may produce between four and six generations per year ([@B61]; [@B34]; [@B52]; [@B28]), which is significantly higher than the rate in regions beyond China. For example, *O. robiniae* produces three to four generations per year in Italy ([@B12]) and Serbia ([@B33]), and a maximum of three generations per year in Korea ([@B26]).

Genetic diversity and population structure are important factors affecting the colonization of invasive species ([@B1]; [@B18]; [@B69]). Invasive species often exhibit low genetic diversity during founding events, as new habitats are typically colonized by only a few individuals, representing a small proportion of the allelic diversity present in the source population ([@B37]; [@B59]). However, when the founding individuals originate from multiple source populations, the genetic diversity of the founder population can be relatively high ([@B9]). This can contribute to invasion success by facilitating local adaptation to new environments and increasing new trait diversity ([@B15]). Besides, the invaders can rapidly evolve in isolation from other individuals of the same species when they were introduced into the new environments ([@B25]; [@B24]).

DNA-based molecular markers have been extensively used to examine the genetic diversity and population structure of a wide range of species. Microsatellite DNA markers (simple sequence repeats, SSRs) are suitable for routine genetic diversity analyses ([@B60]; [@B21]; [@B19]), as they are ubiquitous among eukaryotes ([@B53]), co-dominantly inherited, and highly polymorphic ([@B71]). Moreover, microsatellite analysis can yield valid results and improved phylogenetic trees compared to analyses involving other molecular markers ([@B48]). Due to their feasibility and practicality, microsatellite markers have been widely used in population genetics and ecological studies of various insects ([@B5]; [@B31]; [@B2]; [@B43]; [@B11]; [@B19]; [@B56]), including several invasive gall midge species ([@B4]; [@B1]).

Previously, [@B51] investigated the genetic variation among Chinese *O. robiniae* populations using a partial mitochondrial DNA cytochrome c oxidase subunit I (COI) sequence marker. However, only 10 individuals exhibiting haplotypic variation and a mere four haplotypes were detected in 560 *O. robiniae* samples. Thus, the genetic mechanisms behind successful invasion, the genetic structure in the process of colonization, and the phylogenetic relationships among the Chinese *O. robiniae* populations remain poorly understood.

Accordingly, to gain further insight into the genetic structure of the Chinese *O. robiniae* populations and ascertain how the species has spread widely in new regions, we used 11 microsatellite markers to analyze the genetic structure of 22 Chinese *O. robiniae* populations. Two native populations from the United States (US) were also assessed based on the same loci for comparison with the Chinese populations, in order to explain how genetic diversity is altered during the invasion process.

Materials and Methods {#S2}
=====================

Sample Collection {#S2.SS1}
-----------------

We collected the gall midge larvae and pupae contained within rolled leaves of host trees growing in 22 cities across China ([Figure 1](#F1){ref-type="fig"}). Generally, the rolled leaves were randomly picked from different trees; however, when infestation was low, individual trees were singled out for sample collection. Following collection, the rolled leaves were immediately transported to the laboratory in 60 cm × 40 cm plastic bags, in which they were maintained until adult emergence. From the samples collected at each location, 20 larger adults were selected, placed into a 1.5-mL centrifuge tube, and stored at −20°C for subsequent DNA extraction. Additionally, eight *O. robiniae* adults from two regions of the United States were obtained from the Quarantine Lab at the Institute of Forest Ecology, Environment, and Protection in the Chinese Academy of Forestry. Details of the sample collection and population codes are listed in [Table 1](#T1){ref-type="table"}.

![Geographic locations of the 22 Chinese *Obolodiplosis robiniae* populations sampled. Population codes are listed in [Table 1](#T1){ref-type="table"}. (The source map was downloaded from the website <http://www.webmap.cn/commres.do?method=dataDownload>).](fgene-11-00387-g001){#F1}

###### 

Location of *Obolodiplosis robiniae* populations and the sample size used in this study.

  Number   Code   Site                              Latitude (N)   Longitude (E)   Altitude   Sample size
  -------- ------ --------------------------------- -------------- --------------- ---------- -------------
  \(1\)    BJ     Beijing                           40°00.184′     116°14.363′     76         20
  \(2\)    CC     Changchun, Jilin                  43°53.851′     125°16.329′     218        20
  \(3\)    CD     Chengdu, Sichuan                  30°38.245′     104°07.334′     510        20
  \(4\)    DD     Dandong, Liaoning                 40°06.906′     124°21.536′     33         20
  \(5\)    DL     Dalian, Liaoning                  38°58.531′     121°36.800′     67         20
  \(6\)    DY     Dongying, Shandong                37°26.366′     118°34.448′     17         20
  \(7\)    GY     Guiyang, Guizhou                  26°33.531′     106°45.003′     1090       20
  \(8\)    HF     Hefei, Anhui                      31°52.824′     117°11.639′     39         20
  \(9\)    NJ     Nanjing, Jiangsu                  32°03.426′     118°50.820′     90         20
  \(10\)   QD     Qingdao, Shandong                 36°03.367′     120°20.934′     24         20
  \(11\)   QH     Qinhuangdao, Hebei                39°56.161′     119°35.411′     17         20
  \(12\)   SY     Shenyang, Liaoning                41°50.438′     123°25.690′     51         20
  \(13\)   TA     Taian, Shandong                   36°12.225′     117°07.104′     208        20
  \(14\)   TS     Tianshui, Gansu                   34°21.405′     106°00.034′     1460       20
  \(15\)   TY     Taiyuan, Shanxi                   37°54.592′     112°31.811′     798        20
  \(16\)   WH     Wuhan, Hubei                      30°36.733′     114°17.772′     40         20
  \(17\)   XA     Xian, Shaanxi                     34°15.474′     108°58.938′     428        20
  \(18\)   YA     Yanan, Shaanxi                    36°35.633′     109°29.535′     1121       20
  \(19\)   YC     Yinchuan, Ningxia                 38°28.933′     106°11.983′     1115       20
  \(20\)   YK     Yingkou, Liaoning                 40°12.432′     122°04.413′     15         20
  \(21\)   YT     Yantai, Shandong                  37°32.024′     121°25.657′     9          20
  \(22\)   ZZ     Zhengzhou, Henan                  34°48.509′     113°42.266′     95         16
  \(23\)   US_f   Finger Lakes, NY, United States   42°45′         −76°41.4′W      --         5
  \(24\)   US_g   Goat Island, NY, United States    43°48′         −79°42′W        --         3

DNA Extraction and Microsatellite Analyses {#S2.SS2}
------------------------------------------

Genomic DNA was extracted from the entire *O. robiniae* body following the instructions described by [@B70] and stored at −20°C until needed. The 14 microsatellite loci (W3, W5, W8, W29, W31, W33, W35, W41, W46, W82, W83, W116, W126, and W132) developed by [@B65] were initially selected to analyze the genotypes of 20 individuals per collection site (the exception being Zhengzhou, for which 16 individuals were analyzed) ([Table 1](#T1){ref-type="table"}). For each sample, we attempted to amplify all 14 loci; however, after two attempts, we were unable to amplify five loci (W29, W35, W41, W46, and W116) for many individuals; thus, these loci were not used in subsequent analyses. However, we assessed the applicability of two additional loci (W6 and W107; GenBank numbers: KP260520 and KP260530) that were not characterized by [@B65], and we detected sufficient polymorphism among the analyzed samples. Hence, a total of 11 loci were used to genotype 444 *O. robiniae* individuals.

Microsatellite amplifications were performed in a 15 μL reaction volume containing 1 μL genomic DNA (10 ng), 1 μL of each primer (5 μmol/L), 7.5 μL 2X Taq PCR Master Mix (TIANGEN, Beijing, China), and 4.5 μL ddH~2~O. The forward primer of each primer pair was labeled with a fluorescent dye (HEX, ROX, FAM, or TMARA; Sangon Biotech, Shanghai, China). The microsatellite cycling protocol was: 5 min at 95°C (initial denaturation step); followed by 30 cycles of 94°C for 30 s, 53°C (W3, W5, W6, and W8) or 56°C (the remaining loci) for 45 s, 72°C for 45 s, extension at 72°C for 10 min, and finally maintained at 16°C. PCR products were examined using a DNA analyzer (Applied Biosystems, Waltham, CA, United States) and the results were analyzed using Genotyping was carried out using a 3730xl automated DNA sequencer (Applied Biosystems, Waltham, CA, United States). Alleles were scored using GeneMarker software version 2.2. (Softgenetics LLC, State College, PA, United States).

Data Analyses {#S2.SS3}
-------------

Genetic diversity was estimated by basic statistical analyses including number of alleles (*Na*), effective number of alleles (*Ne*), Shannon's information index (*I*), observed heterozygosity (*Ho*), expected heterozygosity (*He*), and [@B35] expected heterozygosity (*Nei*), which were calculated using GenePop software version 4.3 ([@B45]); genotype number (*GN*), gene diversity (*GD*), and polymorphism information content (*PIC*) were calculated using PowerMarker software version V3.25 ([@B27]). *F*-statistics and gene flow for each locus across populations were performed using PopGene software version 1.32 ([@B66]). Deviations from the Hardy--Weinberg equilibrium (HWE) based on the Markov chain algorithm (10,000 steps) and linkage disequilibrium (LD) (10,000 permutations) were also examined by GenePop software. The genetic relationships between populations were assessed using a neighbor-joining dendrogram generated by PowerMarker and Molecular Evolutionary Genetics Analysis across Computing Platforms (MEGA X) ([@B23]).

Populations differentiation was assessed by pairwise *Fst* values (based on 999 permutations) and gene flow (*Nm*) through AMOVA (analysis of molecular variance) approach which were performed using the Arlequin program version 3.5 ([@B14]). The analyses can estimate variance and partitioning of the within- and among-population. Genetic structure analysis was performed with 100,000 Markov Chain Monte Carlo repetitions after a burn-in period of 200,000 interactions for each group number (*K*) using STRUCTURE software version 2.3.4 ([@B41]). The number of subpopulations (*K*) was assumed to be from 1 to 22, without admixture and with correlated allele frequencies. To determine the most likely number of subpopulations, the optimum *K*-value was obtained by calculating the Δ*K* value ([@B13]).

In addition, the Mantel test was conducted using the GenALEx 6.5 program ([@B39]) to determine correlations between Nei's genetic distance \[was calculated using GenePop software based on [@B36]\] and both geographical distance (km) and altitude (m). Significance was assessed by conducting 999 permutations. Moreover, a principal coordinate analysis (PCoA) was conducted using the same software.

Results {#S3}
=======

Microsatellite Polymorphism and Diversity {#S3.SS1}
-----------------------------------------

In this study, locus polymorphism and diversity were determined based on 22 Chinese populations using 11 microsatellite markers, with each population consisting of 20 individual samples (except Zhengzhou, with 16 samples) ([Table 1](#T1){ref-type="table"}). Amplifying these microsatellite markers loci led to 436 polymorphic bands ([Supplementary Table S1](#TS1){ref-type="supplementary-material"}), representing 202 genotypes, ranging from 3 to 54 per primer pair. As shown in [Table 2](#T2){ref-type="table"}, there were 72 alleles among the 22 populations; the number of alleles (*Na*) observed per locus varied from 2 (W83 and W107) to 14 (W3 and W5), with a mean of 6.5 per locus. The effective numbers of alleles (*Ne*) varied from 1.4087 (W83) to 7.9971 (W3), with an average of 3.7255 per locus. The gene diversity index (*GD*) per locus ranged from 0.2901 (W83) to 0.8755 (W3), with an average of 0.6511, indicating that a high level of information was provided by the 11 microsatellite markers. Shannon's information index (*I*) ranged from 0.4767 (W83) to 2.2573 (W3), with a mean of 1.3188. The polymorphism information content (*PIC*) for the microsatellite loci ranged from 0.2494 (W83) to 0.8627 (W3), with an average of 0.6031. The observed heterozygosity (*Ho*) ranged from 0.2271 (W107) to 0.7477 (W3) and expected heterozygosity (*He*) ranged from 0.2905 (W83) to 0.8760 (W3). For each locus, both *Na* and *Ho* values markedly changed among populations, whereas, *He* value minorly altered ([Supplementary Figure S1](#FS1){ref-type="supplementary-material"}). In addition, when we evaluated the within-sample HWE deviations for each locus across all 22 Chinese populations using the Markov chain algorithm (10,000 steps), we detected significant deviation from the expected value (*p* \< 0.05) in 68 of 242 tests (28.10%) ([Supplementary Table S2](#TS2){ref-type="supplementary-material"}). Examination of genotypic LD between all pairs of alleles across all 22 populations, based on a permutation procedure (10,000 permutations), revealed a significant LD (*p* \< 0.05) in 251 of 1210 tests (20.74%) from 11 loci in the 22 Chinese populations.

###### 

Polymorphism of microsatellite loci across Chinese *O. robiniae* populations.

  Locus   *Na*   *GN*      *Ne*     *GD*     *I*      *Ho*     *He*     *PIC*
  ------- ------ --------- -------- -------- -------- -------- -------- --------
  W3      14     54        7.9971   0.8755   2.2573   0.7477   0.8760   0.8627
  W5      14     42        5.8768   0.8295   1.9743   0.7067   0.8308   0.8085
  W6      4      6         2.2687   0.5601   0.9575   0.3471   0.5599   0.4973
  W8      7      18        4.3609   0.7712   1.6184   0.3701   0.7716   0.7367
  W31     5      14        4.2747   0.7661   1.5224   0.6697   0.7669   0.7281
  W33     7      20        3.6391   0.7249   1.4660   0.6506   0.7260   0.6835
  W82     5      12        3.3448   0.7010   1.3193   0.3532   0.7018   0.6487
  W83     2      4         1.4087   0.2901   0.4767   0.3005   0.2905   0.2494
  W107    2      3         1.6205   0.3829   0.5710   0.2271   0.3833   0.3096
  W126    4      6         2.0018   0.5005   0.7223   0.4398   0.5010   0.3809
  W132    8      23        4.1874   0.7603   1.6214   0.4207   0.7621   0.7284
  Mean    6.5    18.3636   3.7255   0.6511   1.3188   0.4757   0.6518   0.6031

Number of alleles (Na), genotype number (GN), effective number of alleles (Ne), gene diversity (GD), Shannon's information index (I), observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC).

Population Genetic Diversity {#S3.SS2}
----------------------------

The genetic diversity of 22 Chinese populations and two US populations was assessed. Six indices of genetic diversity (*Na*, *Ne*, *I*, *Ho*, *He*, and *Nei*) were evaluated. As shown in [Table 3](#T3){ref-type="table"}, for each Chinese population across all loci, the means of the above indices except *Na* were moderately or considerably lower than those of the native US populations, although the sample size of the Chinese populations (40) was markedly higher than that of the US populations (8). Regarding the three most important indices, *I*, *He*, and *Nei*, the lowest Chinese values (*I* = 0.7847, *He* = 0.4279, *Nei* = 0.4172) occurred in the DY population, and the highest (*I* = 1.1103, *He* = 0.6162, *Nei* = 0.6008) in the TS population. Increased values of these three indices occurred in the SY (*I* = 1.058, *He* = 0.6157, *Nei* = 0.6003), YT (*I* = 1.0761, *He* = 0.5825, *Nei* = 0.5677), and DD (*I* = 1.016, *He* = 0.5895, *Nei* = 0.5748) populations. The inbreeding coefficient (*Fis*) ranged from −0.0295 (TY) to 0.2011 (CD), with significant various observed for each locus among populations. Both US populations exhibited high *He* values of 0.6544 (US_f) and 0.6606 (US_g).

###### 

Genetic diversity of the *O. robiniae* populations across 11 microsatellite loci.

  Code      Sample size   *Na*     *Ne*     *I*      *Ho*     *He*     *Nei*    *Fis*
  --------- ------------- -------- -------- -------- -------- -------- -------- ----------
  BJ        40            3.3636   2.375    0.868    0.4591   0.4928   0.4805   0.0445
  CC        40            3.7273   2.5568   0.9673   0.4364   0.5425   0.529    0.1751
  CD        40            3.4545   2.1684   0.8976   0.4136   0.531    0.5177   0.2011
  DD        40            3.5455   2.651    1.016    0.5591   0.5895   0.5748   0.0273
  DL        40            3.1818   2.3375   0.8675   0.5636   0.5026   0.49     --0.1503
  DY        40            3.2727   2.2412   0.7847   0.4409   0.4279   0.4172   --0.0569
  GY        40            3.5455   2.0894   0.8164   0.3864   0.4705   0.4588   0.1578
  HF        40            3.1818   2.5997   0.9554   0.4909   0.5742   0.5599   0.1232
  NJ        40            3.8182   2.4202   0.9633   0.4364   0.5408   0.5273   0.1724
  QD        40            3.3636   2.2096   0.8265   0.3682   0.4647   0.4531   0.1874
  QH        40            3.6364   2.2661   0.9192   0.5318   0.5233   0.5102   --0.0423
  SY        40            3.7273   2.797    1.058    0.5455   0.6157   0.6003   0.0914
  TA        40            3.3636   2.0576   0.8347   0.439    0.4792   0.4672   0.0603
  TS        40            4.0909   2.8428   1.1103   0.5045   0.6162   0.6008   0.1602
  TY        40            3.6364   2.3278   0.9101   0.5273   0.5253   0.5122   --0.0295
  WH        40            3.7273   2.5723   0.9797   0.5256   0.5599   0.5459   0.0371
  XA        40            3.5455   2.535    0.9503   0.5182   0.5492   0.5355   0.0323
  YA        40            3.8182   2.7608   0.9798   0.4199   0.5355   0.5221   0.1958
  YC        40            3.8182   2.4284   0.9586   0.467    0.5369   0.5234   0.1078
  YK        40            4.4545   2.6524   1.0623   0.4682   0.567    0.5528   0.1531
  YT        40            4.2727   2.9205   1.0761   0.5081   0.5825   0.5677   0.1049
  ZZ        30            3.4545   2.3364   0.912    0.4516   0.5346   0.5168   0.1261
  CN mean   40            3.6363   2.4612   0.9415   0.4755   0.5346   0.5192   --
  US_f      10            4.0909   3.2336   1.1472   0.5      0.6544   0.5875   0.1489
  US_g      6             3.0909   2.6363   0.9632   0.5152   0.6606   0.5505   0.0642
  US mean   8             3.5909   2.935    1.0552   0.5076   0.6575   0.569    --

Number of alleles (Na), effective number of alleles (Ne), Shannon's information index (I), observed heterozygosity (Ho), expected homozygosity (He),

Nei's (1973)

expected heterozygosity (Nei), inbreeding coefficient (Fis).

Genetic Differentiation in the Chinese Populations {#S3.SS3}
--------------------------------------------------

The *Fst* per locus ranged from 0.1357 to 0.3770, with an average of 0.1994, and the *Fis* per locus ranged from −0.0037 (W3) to 0.3364 (W82) with an average of 0.0873 alleles per locus across populations ([Table 4](#T4){ref-type="table"}). Gene flow (*Nm*) ranged from 0.3093 at W31 to 1.5921 at W33 and averaged 1.0036. Meanwhile, the pairwise *Fst* (*p* \< 0.001) values ([Supplementary Table S3](#TS3){ref-type="supplementary-material"}) between populations ranged from 0.022 (HF and WH) to 0.377 (BJ and GY), with an average value of 0.183. A total of 135 of the 213 *Fst* values (63.38%) were \>0.15, while 48 (22.54%) were \>0.25, which suggests that significant genetic differentiation exists among the sampling sites and there is some restriction in gene flow between them ([Table 4](#T4){ref-type="table"}). The most noticeable genetic differentiation occurred between BJ and GY (*Fst* = 0.377), followed by GY and QH (*Fst* = 0.372), then DY and QH, DY and GY (*Fst* = 0.361 for both). According to the coefficient of genetic differentiation (*Fst* = 0.1830, *p* \< 0.001), genetic variation within populations (81.66%) was substantially higher than that among populations (18.34%) (*p* \< 0.001) ([Table 5](#T5){ref-type="table"}). Gene flow (*Nm*) ranged from 0.414 (BJ and GY) to 11.05 (HF and WH) ([Supplementary Table S4](#TS4){ref-type="supplementary-material"}), with an average of 1.113 ([Table 5](#T5){ref-type="table"}). All investigated loci contributed to the population differentiation (*p* \< 0.001 for each individual locus). Regarding the native US populations, they had a relatively low *Fst* value (0.085, *p* \< 0.03), a high *Nm* value (2.685), and variation within populations of 91%, while variation among populations was 9% (*p* \< 0.02). This further indicates the existence of extensive genetic differentiation among the introduced populations.

###### 

Summary of *F* statistics and gene flow for each locus.

  Locus   *Fis*      *Fst*    *Nm*
  ------- ---------- -------- --------
  W3      --0.0037   0.1487   1.4307
  W5      0.0026     0.1460   1.4629
  W6      0.0040     0.3770   0.4130
  W8      0.2929     0.3198   0.5317
  W31     --0.0441   0.1603   1.3093
  W33     --0.0364   0.1357   1.5921
  W82     0.3364     0.2379   0.8007
  W83     --0.1980   0.1367   1.5792
  W107    0.3182     0.1402   1.5337
  W126    --0.0165   0.1379   1.5634
  W132    0.2870     0.2230   0.8745
  Mean    0.0873     0.1994   1.0036

Gene flow (Nm) estimated based on Nm = 0.25(1 -- Fst)/Fst.

###### 

Population genetic variance revealed by 11 microsatellite loci through AMOVA analysis.

  Source                    Degree of freedom   Sum of squared deviations   Mean squared deviations   Variance component estimates   Percentage of variation
  ------------------------- ------------------- --------------------------- ------------------------- ------------------------------ -------------------------
  **Chinese populations**                                                                                                            
  Among populations         21                  622.846                     29.659                    0.665                          18%
  Among individuals         414                 1371.394                    3.313                     0.353                          10%
  Within individuals        436                 1136.500                    2.607                     2.607                          72%
  Total                     871                 3130.740                                              3.624                          100%
  *Fst*                     0.183               0.001                                                                                
  *Nm*                      1.113                                                                                                    
  *P*-value                 \<0.001                                                                                                  
  **US populations**                                                                                                                 
  Among populations         1                   7.496                       7.496                     0.354                          9%
  Among individuals         6                   29.067                      4.844                     1.047                          25%
  Within individuals        8                   22.000                      2.750                     2.750                          66%
  Total                     15                  58.563                                                4.151                          100%
  *Fst*                     0.085                                                                                                    
  *Nm*                      2.685                                                                                                    
  *P*-value                 \<0.03                                                                                                   

Genetic Relationships and Population Structure Analysis {#S3.SS4}
-------------------------------------------------------

A dendrogram depicting the genetic relationships among the 22 Chinese populations was constructed based on the microsatellite data ([Figure 2](#F2){ref-type="fig"}). The populations were divided into two main clusters, and each cluster was further separated into several sub-clusters. Group I contained populations from Northeast China (Jilin Province) to Southwest China (Guizhou Province), including Liaoning (YK, DL), Jilin (CC), Shanxi (TY), Shaanxi (YA), Shandong (DY, QD), Henan (ZZ), Sichuan (CD), Hubei (WH), Anhui (HF), and Guizhou (GY) provinces. Group II contained populations from North China (with the exception of the NJ population), including Beijing (BJ), Liaoning (SY, DD), Hebei (QH), Shandong (YT, TA), Shaanxi (XA), Gansu (TS), Ningxia (YC), and Jiangsu (NJ) provinces. Besides, some subdivided populations were clustered according to their spatial distribution, such as GY, HF, and WH located in the south of southern China, which clustered together in group I, whereas TA, DD, BJ, and QH located around Bohai Bay clustered together in group II.

![Unrooted neighbor-joining dendrogram of the 24 *O. robiniae* populations based on Nei's distance using the allele frequencies of 11 microsatellite loci. Green represents the subpopulations of group 1, red represents the subpopulations of group 2, and blue represents the US populations.](fgene-11-00387-g002){#F2}

The 436 Chinese *O. robiniae* samples were further assessed for population stratification using STRUCTURE software. Microsatellite data were analyzed with possible cluster numbers (*K*-values) ranging from 1 to 22. Δ*K* was clearly maximized when *K* = 2 (Δ*K* = 4298.3743), indicating the occurrence of two distinct groups among the 22 populations ([Figure 3](#F3){ref-type="fig"}), which validates the dendrogram-based grouping, and the second clade was grouped according to approximate geographical area. These results suggested different degrees of introgression in the populations, detected as differences in allelic frequencies among the populations. In addition, greater structuring (*K* = 3) revealed that QD, GY, and TY in group I had certain structural similarities to YT, SY, NJ, YC, and XA in group II.

![Graphical output of the STRUCTURE analysis representing hierarchical data analyses to determine the number of genetic subpopulations (*K*) of *O. robiniae*. Each individual is represented by a single vertical bar.](fgene-11-00387-g003){#F3}

In addition, PCoA based on the marker genotypes also revealed two distinct clusters of the Chinese populations ([Figure 4](#F4){ref-type="fig"}), which were partly related to their geographical regions (group II contained populations from North China). The Mantel test revealed non-significant negative correlations between Nei's genetic distance ([Supplementary Table S5](#TS5){ref-type="supplementary-material"}) and geographical distance (km) (*r* = −0.02, *P* = 0.456; [Supplementary Figure S2](#FS2){ref-type="supplementary-material"}), and between Nei's genetic distance and altitude (*r* = −0.026, *P* = 0.419; [Supplementary Figure S3](#FS3){ref-type="supplementary-material"}), which indicates that genetic differentiation in the 22 Chinese populations may not be caused by geographical isolation.

![Principal coordinate analysis (PCoA) of 436 Chinese *O. robiniae* individuals showing two distinct clusters of populations. Each population is represented by a diamond. Coord.1 (39.51%) and Coord.2 (17.63%) refer to the first and second principal component, respectively.](fgene-11-00387-g004){#F4}

Discussion {#S4}
==========

In the present study, we detected a high degree of polymorphism among the assessed microsatellite loci. We also identified a relatively high level of genetic diversity among Chinese *O. robiniae* populations across all loci, with the average expected heterozygosity (He) and [@B35] expected heterozygosity (*Nei*) being 0.5346 and 0.5192, respectively. The highest *He* was 0.6606, which occurred in the US_g population despite the fact that it consisted of only three *O. robiniae* individuals. Nonetheless, *He*, *Nei* ([@B49]), gene diversity index (*GD*), and polymorphism information content (*PIC*) ([@B42]) are minimally influenced by sample size. Our results indicate significant differences in genetic diversity within the Chinese populations, as well as between the native and invasive populations. [@B51] detected a relatively low level of genetic diversity among Chinese *O. robiniae* populations using a COI marker. This discrepancy suggests that COI markers may be less suitable than microsatellite DNA markers for population analyses of a new invasive species, such as Chinese *O. robiniae*, as is the case with another invasive cecidomyiid, *Procontarinia mangiferae* ([@B1]).

The relatively high level of genetic diversity has likely contributed to the spread of *O. robiniae* across China in the past decade to the extent that this species is now established in most regions where its host exists ([@B50]). *O. robiniae* has a short history in China, with initial detection occurring in 2004 ([@B64]), whereas its host was introduced over a century ago ([@B63]). Moreover, for the 22 Chinese populations analyzed, our results show a relatively high level of genetic differentiation (*Fst* = 0.1830) among populations from sites separated by distances of up to 2,540 km. Hence, the relatively high genetic diversity likely caused by significant differentiation among populations has been conducive to the rapid colonization and establishment of *O. robiniae* in China, and it is an important factor contributing to the successful invasion of *O. robiniae*.

The observed population differentiation likely resulted from rapid evolution during adaptation to the new environment. Invasive species may evolve rapidly in response to selection pressures driven by novel habitats ([@B47]; [@B25]; [@B38]), and such rapid genetic adaptation might be important for invasive species ([@B54]) in order to increase fitness and invasion success ([@B58]; [@B46]). Increasing the success of both their initial establishment and subsequent range expansion is a particularly effective strategy for introduced populations, as was shown for several invasive species during their colonization processes ([@B55]; [@B38]; [@B62]). Hence, differentiation among Chinese populations was likely accelerated by the rapid evolution of adaptations to the new environments, promoting successful invasion by *O. robiniae*.

Furthermore, high levels of genetic diversity in an invasive species might be caused by multiple introductions or large founding populations. It is thought that multiple introductions are associated with increased diversity because they supply increased variation and new genetic communities ([@B10]). Multiple introductions are considered to produce invasive populations that are much more genetically diverse than a single source population ([@B47]). As such, the successful establishment and invasion of many invasive species have been attributed to multiple introductions ([@B30]; [@B20]; [@B7]; [@B67]; [@B32]). For *O. robiniae*, in light of the short history in China, its high genetic diversity and colonization success might be also related to multiple independent invasive events.

Many studies have shown that the genetic structures of invasive species are well developed in their new ranges ([@B68]; [@B17]; [@B44]; [@B67]). Indeed, the genetic diversity of invasive species is often higher than that of native populations ([@B29]). In light of this, our neighbor-joining dendrogram and Bayesian STRUCTURE (*K* = 2) analyses indicated that the Chinese *O. robiniae* populations are divided into two independent clusters, although this division appears to be unrelated to geographical distribution. Meanwhile, gene flow was found among some Chinese populations, with QD, GY, and TY in group I having highly similar structures to YT, SY, NJ, YC, and XA in group II. In addition, some subgroups (GY, HF, and WH; TA, DD, BJ, and QH) were clustered according to their geographical distribution, which likely represents different routes of spread in the new environment. Furthermore, our results revealed that each group of the Chinese *O. robiniae* populations exhibited differences in geographical distribution and genetic distance, suggesting that the two groups do in fact represent two different sources. This implies the introduced populations likely experienced two independent invasive events, which initially shaped the genetic structure of the Chinese *O. robiniae* populations.

On the other hand, the fact that the division of the two groups was unrelated to geographical distribution (particularly for group I, which contained numerous genetically similar populations located in different geographical regions) suggests that human activity is likely another important contributing factor. Transport, business trips, and long-distance vacations have recently increased not only in frequency but also in distance. High levels of human-mediated dispersion can increase the genetic diversity of an invasive population, thereby substantially modifying the genetic structure and potential management units ([@B40]); these factors decrease the success of control measures for *O. robiniae* populations.

Data Availability Statement {#S5}
===========================

All datasets generated for this study are included in the article/[Supplementary Material](#FS1){ref-type="supplementary-material"}.

Author Contributions {#S6}
====================

Y-XY and W-XZ: conceptualization, funding acquisition, project administration, and supervision. R-ZL, W-XH, and JY: data curation. Y-XY, X-PS, and JY: formal analysis, investigation, and resources. Y-XY and X-PS: methodology. Y-XY and R-ZL: software. Y-XY, R-ZL, and W-XH: validation. Y-XY, X-PS, R-ZL, and W-XH: visualization. Y-XY, X-PS, R-ZL, W-XH, JY, and W-XZ: writing -- original draft and review and editing.

Conflict of Interest {#conf1}
====================

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Funding.** This work was funded by the Fundamental Research Funds of the Chinese Academy of Forestry (CAFYBB2017SZ003) and the National Key R&D Program of China (2016YFC1201200).

We would like to thank Dr. Yan-Fei Zeng (Research Institute, Chinese Academy of Forestry) for discussions regarding the data. We also sincerely thank the other members of the Plant Quarantine Lab and Plant Pathology Lab of the Chinese Academy of Forestry for their helpful comments.

Supplementary Material {#S9}
======================

The Supplementary Material for this article can be found online at: <https://www.frontiersin.org/articles/10.3389/fgene.2020.00387/full#supplementary-material>

###### 

Click here for additional data file.

###### 

Click here for additional data file.

###### 

Click here for additional data file.

###### 

Click here for additional data file.

###### 

Click here for additional data file.

###### 

Click here for additional data file.

###### 

Click here for additional data file.

###### 

Click here for additional data file.

[^1]: Edited by: Wei Guo, Institute of Zoology (CAS), China

[^2]: Reviewed by: Chuan Ma, Chinese Academy of Agricultural Sciences, China; Jagadish S. Bentur, Agri Biotech Foundation, India

[^3]: This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics
