Biol Invasions (2013) 15:447–458 DOI 10.1007/s10530-012-0299-5
ORIGINAL PAPER
Genetic structure and diversity of a newly invasive species, the codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae) in China Qiu-Lei Men • Mao-Hua Chen Ya-Lin Zhang • Ji-Nian Feng
•
Received: 14 November 2011 / Accepted: 25 July 2012 / Published online: 8 August 2012 Ó Springer Science+Business Media B.V. 2012
Abstract Cydia pomonella (L.) was firstly reported in China in the 1950s and considered as one of the most serious invasive pest in fruit orchards of China. It spread rapidly from the original site in Xinjiang to other northwestern regions. The pest has further penetrated northeastern China since 2006. With its rapid invasion rate, most pome fruit production areas of China are being threatened. As yet there has been no research into the genetic diversity and structure of the codling moth population in China. We investigated the genetic variations of 12 C. pomonella populations collected from the main distribution regions (Xinjiang, Gansu and Heilongjiang Provinces) in China and compared them with one German and one Swiss population using eight microsatellites loci to infer the characteristics of genetic diversity and genetic structure. We observed sequential loss of genetic diversity and significant structuring associated with distribution but no significant correlation between genetic distance and geographic distance among northwestern populations. There was no genetic evidence for bottleneck effects in any of the populations. The results suggest that the loss of genetic diversity in C. pomonella
Q.-L. Men M.-H. Chen Y.-L. Zhang J.-N. Feng (&) Key Laboratory of Plant Protection Resources and Pest Management of Ministry of Education, Entomological Museum, Department of Entomology, Northwest A & F University, Yangling 712100, Shaanxi, China e-mail:
[email protected]
populations resulted from the successive colonization of founder populations. Recent invasion history led to the lack of any bottleneck effect. The high level of population genetic structuring is related to the weak flight capacity of the codling moth and the humanaided dispersal rather than to geographic distance. These genetic data not only provide us with an understanding of the micro-evolutionary processes related to successful biological invasions, but also provide guidance for pest management strategies. Keywords Cydia pomonella Invasive Microsatellites Genetic structure Genetic diversity China
Introduction Many factors influence the genetic structure of animal populations. For an invasive species, the factors which influence population genetic structure are often related to its history of invasion (Grapputo et al. 2005; Watts et al. 2010). Populations that have been in stable environments and are connected by dispersal over long periods of time will reach a genetic equilibrium where the loss of alleles as a result of drift is balanced by the introduction of new alleles through migration (Wright 1951). In contrast, in newly established populations, genetic structure is often determined by the historical events, such as bottleneck or founder effects, vicariance events and range of dispersal, rather
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than gene flow and genetic drift (Bohonak 1999). The levels of genetic diversity and genetic structure for the newly invasive populations are affected by the recent expansion of the species, the numbers of founders and passive dispersal by anthropic factors (Ramstad et al. 2004; Dlugosch and Parker 2008; Watts et al. 2010). The codling moth, Cydia pomonella (L.), is a destructive fruit pest in fruit orchards (Barnes 1991; Willett et al. 2009). This moth’s rapid spread to nearly all apple growing areas throughout the world has largely been due to a combination of the growing popularity of pome fruit culturing and accidentally human-aided dispersal (Boivin et al. 2004; Meraner et al. 2008). Host plants for the codling moth include apple, pear, apricot, plum, peach, nectarine, cherry, quince and walnut (Barnes 1991). The first reported sightings of the codling moth in China were in 1957 (Zhang 1957). It has now been observed throughout Xinjiang which covers more than 1.6 million square kilometers of mostly mountains, deserts and unpopulated areas. From this first site, it took more than 20 years to reach Gansu, spreading along the Hexi Corridor. In 2006, new invasion sites were found in Heilongjiang, the first report in northeastern China. As a notoriously invasive pest, the codling moth has caused a great deal of damage to Chinese production of pomes (primarily apple and pear) since it was first reported, giving rise to substantial economic losses for affected farmers every year (Wan et al. 2009). A better understanding of the micro-evolutionary processes and ecological strategies related to successful biological invasions is necessary in order to develop effective pest management practices (Dorn et al. 1999; Miller et al. 2003). Previous researchers focused on the genetic diversity and genetic structure of older C. pomonella populations from Europe and Africa (Timm et al. 2006; Espinoza et al. 2007; Franck et al. 2007; Fuentes-Contreras et al. 2008; Chen and Dorn 2009; Franck and Timm 2010), but there has been no investigation of the population genetic structure and genetic diversity of the codling moth in China. Molecular markers have greatly facilitated the study of population genetic structure and diversity. Microsatellites, AFLP and mitochondrial genes have been successfully used to characterize many insect species (De Barro 2005; Paupy et al. 2005; Mozaffarian et al. 2007; Meng et al. 2008; Orsini et al. 2008; Ahern et al. 2009; Cristescu et al. 2010). In this study, we use eight microsatellite loci to investigate the
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genetic diversities and genetic structures of 12 populations collected from the main distribution regions of C. pomonella in China. Our main objectives were to analyze the genetic diversity and genetic structure of codling moths in newly-invaded ecosystems in China.
Materials and methods Sampling A total of 339 Cydia pomonella individuals were collected from 12 apple orchards in the C. pomonella distribution regions of China from 2008 to 2010. In Xinjiang, an infested fruit was collected from each fruit tree per orchard and 1 second or third instar larva per fruit was used. In the newly invaded Gansu and Heilongjiang, more than 30 pheromone traps were used in each apple orchard ([5 ha), and only one moth from each trap was used. All the sample locations are illustrated in Fig. 1. For making comparisons with populations in China, C. pomonella samples collected by pheromone traps in Germany (samples from Karlsruhe in July, 2008) and Switzerland (samples from Chur in June, 2009) were included in the analysis. All the samples were stored at -20 °C prior to genetic analyses. DNA extraction and microsatellite genotyping Genomic DNA was extracted from the thorax and leg of each individual sample using DNeasy Tissue Kit (QIAGEN, Hilden, Germany). Extraction was performed according to the bench protocol for animal tissues. Before extraction, xylene was used to dissolve the glue covering specimens taken from pheromone traps. Eight loci (Cp1.62, Cp2.39, Cp2.P, Cp3.56, Cp3.169, Cp3.K, Cp4.56 and Cp4.129) were selected for the present study, based on their characterization in the literature as highly polymorphic (Franck et al. 2005). Each locus was amplified using fluorescently-labeled primers (FAM) (Schuelke 2000). Polymerase chain reactions (PCR) were performed using a S1000 Thermal Cycler (BIO-RAD, Hercules, CA, USA) in a total volume of 25 ll, containing 19 PCR amplification buffer (Takara, Dalian, China), 2 mM MgCl2, 0.2 mM of each dNTP (Takara, Dalian, China), 0.2 lM of each forward primer, 0.8 lM of each reverse primer, 0.8 lM M13 primer, 1.0 U Taq polymerase (Takara, Dalian, China), and 1 ll genomic DNA (10–30 ng/ll).
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Fig. 1 a Distribution regions and sample locations for the 12 C. pomonella populations from China, the codes for C. pomonella populations are explained in Table 1; the light gray region represents the geographic distribution of C. pomonella in Xinjiang; the dark gray region represents the geographic distribution in Gansu; the black region represents the
geographic distribution in Heilongjiang; b sample locations of the two populations from Heilongjiang; c sample locations of the 10 populations from Xinjiang and Gansu. Pie charts show the mean membership fractions to each of the four genetic clusters in the populations by cluster analysis using STRUCTURE software
PCR amplification was employed with denaturation at 95 °C for 10 min, followed by 30 amplification cycles consisting of 95 °C for 30 s, 45 s at the primer-specific annealing temperature, 72 °C for 45 s, then 8 cycles consisting of 95 °C for 30 s, 53 °C for 45 s and 72 °C for 45 s, and a final step at 72 °C for 10 min. To examine the length and genotype of the amplified PCR products, an ABI3730XL automated DNA sequencer (Applied Biosystems, Foster City, CA, USA) and GENESCAN version 4.0 (Applied Biosystems, Foster City, CA, USA) were used.
Data analysis Population genetic diversity We used Micro-Checker version 2.2.3 (Van Oosterhout et al. 2004) to check the data for null alleles, stutter-errors and large allele drop-out. Using MICROSATELLITE ANALYSER (MSA) version 3.15 (Dieringer and Schlo¨tterer 2003), we calculated number of alleles, and observed versus expected heterozygosity. Using GENEPOP version 4.0.1
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(Rousset 2008), we estimated the Hardy–Weinberg equilibrium (HWE), HWE-P and genotypic linkage disequilibrium. Allelic Diversity Analyzer (ADZE) version 1.0 (Szpiech et al. 2008) was utilized for calculating the mean allelic richness per locus for all the sampling populations with a rarefaction method that adjusted the observed number of alleles to a common size for each population sample. We used BOTTLENECK version 1.2.02 (Cornuet and Luikart 1996; Piry et al. 1999) to assess deviation from expected heterozygote excess relative to allelic diversity across all eight loci, based on the loss of allelic diversity exceeding heterozygosity during a bottleneck effect (Nei et al. 1975; Hufbauer et al. 2004; Pruett and Winker 2005). Two appropriate models, stepwise mutation model (SMM) and twophase model (TPM) were utilized for this analysis (Di Rienzo et al. 1994; Primmer et al. 1998). Probability values were determined using one-tailed Wilcoxon tests for heterozygote excess (Luikart et al. 1998; Piry et al. 1999). Values of P less than 0.05 were considered significant. The significances were corrected for multiple comparisons by the Bonferroni method (Rice 1989) using SPSS version 19.0 (SPSS Inc. Chicago). Population genetic structure Population structure was analyzed using STRUCTURE version 2.3.3 (Pritchard et al. 2000) based on Bayesian clustering approach with a burn-in period of 50,000 iterations and one million Markov chain Monte Carlo (MCMC) repetitions. We used the admixture ancestry model and the correlated allele frequency model. We performed 20 independent runs for each K to confirm consistency across runs, tested K from 1 to 10 and DK methods (Evanno et al. 2005) was used to calculate the number of genetic clusters (K). The graphical display of genetic structure was produced by DISTRUCT (Rosenberg 2004). Analysis of molecular variance (AMOVA) was performed using ARLEQUIN version 3.0 (Excoffier et al. 2005), along with calculating the inbreeding coefficients (FIS), the pair fixation indices (FST) and their significance with 10,000 permutations (Weir and Cockerham 1984). The significances of fixation indices were corrected for multiple comparisons by the Bonferroni method (Rice 1989) using SPSS version 19.0 (SPSS Inc. Chicago). For AMOVA, samples were
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arbitrarily grouped according to the collection regions: (a) Xinjiang Province; (b) Gansu Province; (c) Heilongjiang Province; and (d) western Europe. Referring to the criterion for genetic differentiation by Wright (1978), we defined genetic differentiation as low for FST \ 0.05, moderate for 0.05 \ FST \ 0.15, high for 0.15 \ FST \ 0.25, and very high for FST [ 0.25. In order to test for isolation by distance (IBD), the matrices of genetic distance FST/(1-FST) and the geographic distance (ln) between 10 populations from northwestern China as well as all 12 Chinese sampling populations were compared using the Mantel test with 10,000 permutations (Mantel 1967). This analysis was performed using the ZT software package (Bonnet and Van der Peer 2002).
Results Microsatellite markers and genetic diversity We genotyped 339 individuals for eight microsatellite loci. Population statistics for all investigated populations are given in Table 1. All eight selected loci were polymorphic and the number of alleles per locus ranged from four to 16. A 21.9 % disequilibrium was found in 392 pairs of loci, but did not appear at certain pairs of loci, which was not considered to be a result of physical linkage. The frequency of null alleles ranged from 0.010 to 0.203, values typical for lepidopterans (Dakin and Avise 2004; Megle´cz et al. 2004). Twelve samples with two non-amplified loci were omitted. The mean number of alleles per locus ranged from 4.3 to 12.6 (Table 2). Two populations from Heilongjiang Province in northeastern China had the largest number of alleles (Don, 12.6; Mud, 10.6). Of populations from northwestern China, the Ili population showed the highest value of mean number of alleles (9.6), followed by the Urumqi population (8.6) and Kuytun population (8.4). In comparison with the Chinese codling moth populations, the German and Swiss population had a relatively high mean number of alleles: 8.6 and 10.4 respectively. The mean value of observed heterozygosity was between 0.201 and 0.550, as compared to the expected heterozygosity of between 0.395 and 0.821. The two populations from Heilongjiang had the largest observed heterozygosity (Mud, 0.517; Don, 0.482), followed by populations from the north of Xinjiang (Kuy, 0.387; Ili, 0.356; Jin, 0.321).
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Table 1 Population details of 12 Cydia pomonella populations collected from the main distribution regions in China and two populations from other countries including collection region, location, location code, longitude, latitude and collection date Collection region
Location
Location code
Longitude (°E)
Latitude (°N)
Collection date
Gansu Province
Zhangye
Zha
100.38
38.85
Mar. 2010
Jiuquan
Jiu
98.42
39.67
Mar. 2010
Dunhuang
Dun
95.50
41.58
Jul. 2009
Ili
Ili
81.32
43.95
Aug. 2008
Jinghe
Jin
83.53
45.12
Aug. 2008
Kuytun
Kuy
84.93
44.45
Aug. 2008
Urumqi
Uru
87.47
43.90
Aug. 2008
Kumul
Kum
93.67
42.83
Aug. 2008
Korla
Kor
86.13
41.60
Aug. 2008
Kashgar
Kas
76.02
39.53
Aug. 2008
Dongning
Don
131.23
44.10
Aug. 2010
Mudanjiang
Mud
129.57
44.58
Aug. 2010
Switzerland (Chur)
Swi
9.52
46.85
Jun. 2009
Germany (Karlsruhe)
Ger
8.40
49.02
Jul. 2008
Xinjiang Province
Heilongjiang Province Other countries
Table 2 Population statistics for Cydia pomonella investigated using eight microsatellite loci Population code
N
NA
r
FIS
Ho
He
HWE-P
TPM
SMM
Zha
25
5.3
5.14
0.484
0.201
0.416
0.006
0.99023
1.00000
Jiu
25
4.3
4.17
0.298
0.272
0.434
0.118
0.90234
0.98633
Dun Kum
33 33
5.6 6.0
5.50 6.13
0.400 0.257
0.220 0.298
0.460 0.495
0.006 0.004
0.97266 0.99609
0.99414 0.99609
Kas
25
5.5
5.43
0.330
0.232
0.395
0.020
0.90234
0.98047
Kor
11
4.9
4.46
0.097
0.290
0.475
0.268
0.96094
0.98828
Uru
26
8.6
8.30
0.486
0.262
0.552
0.125
0.98633
0.99609
Jin
21
6.9
6.82
0.267
0.321
0.519
0.211
0.99414
0.99805
Ili
27
9.6
9.16
0.354
0.356
0.616
0.064
0.98633
0.99805
Kuy
26
8.4
8.08
0.275
0.387
0.537
0.054
0.99805
1.00000
Mud
24
10.6
10.38
0.295
0.517
0.821
0.084
0.62891
0.99609
Don
24
12.6
12.23
0.293
0.482
0.791
0.160
0.98047
0.98047
Ger
15
8.6
7.96
0.259
0.550
0.770
0.192
0.52734
0.98047
Swi
24
10.4
9.63
0.304
0.502
0.801
0.105
0.52734
0.99023
This table includes population code, sample size (N), allelic richness (r), mean number of alleles per locus (NA), multilocus estimate of inbreeding coefficient (FIS), observed heterozygosity (Ho), excepted heterozygosity (He), P value for Hardy–Weinberg equilibrium (HWE) analysis, and P value for heterozygote excess using two phase model (TPM) and stepwise mutation model (SMM). Significant departures from Hardy–Weinberg equilibrium are given in bold, P \ 0.05. NA, FIS, Ho, He and HWE-P are all indicated by mean values over eight loci. Probability values for heterozygote excess were determined using one-tailed Wilcoxon tests, and P values less than 0.05 were considered significant. The significances were tested for multi comparisons by the Boferroni method for k = 14, P \ 0.05
The German and Swiss population also showed relatively high level of observed heterozygosity (Ger, 0.550; Swi, 0.502). Four of the 14 populations revealed
significant departures from HWE, together with high mean values of FIS ranging from 0.097 to 0.486, indicating the existence of heterozygote deficiencies (Table 2).
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the three populations from the Hexi Corridor were at the same level as the population from the adjacent region of Xinjiang (Kum). Probability values testing for a significant heterozygote excess based on SMM and TPM models of microsatellite allele mutation using BOTTLENECK software are listed in Table 2. Results show no significant heterozygosity excess in any population. All P values remained insignificant (P \ 0.05) after Bonferroni correction. Genetic structure Fig. 2 The mean number of distinct alleles per locus for 14 sample populations. The lines represent the trend in the fluctuations of allelic richness accompanied by the change of sample size for each population
Among 12 Chinese populations, two populations from the Heilongjiang had the highest mean allelic richness (Mud, 10.38; Don, 12.23), followed by four populations from northern Xinjiang (Ili, 9.16; Uru, 8.30; Kuy, 8.08; Jin 6.82). Two populations from southern Xinjiang (Kor, 4.46; Kas, 5.43) and three populations from Gansu (Zha, 5.14; Dun, 5.50; Jiu, 4.17) had the relatively low mean allelic richness (Fig. 2). Based on the mean number of alleles per locus, the observed heterozygosity and the fluctuation of allelic richness, the populations from Heilongjiang (Mud and Don) had higher genetic diversity than populations from Xinjiang and Gansu. Populations from northern Xinjiang (Ili, Jin, Kuy and Uru) had similar genetic diversities, and the two populations from southern Xinjiang (Kas and Kor) were similar. Genetic diversities of populations from northern Xinjiang were higher than populations in southern Xinjiang. Meanwhile, population genetic diversities of
Fig. 3 Proportion of membership coefficient with K = 4 for 14 populations of codling moth obtained using STRUCTURE software
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We performed cluster analysis using STRUCTURE version 2.3.3, which simultaneously identifies clusters and assigns individuals to populations using a Bayesian approach. Following the DK methods (Evanno et al. 2005), we obtained the most likely K value of four. All populations in this study were assigned to four clusters which were indicated by different colors and named as cluster 1, cluster 2, cluster 3 and cluster 4. The proportions of each population that contributed to each of the four clusters are shown in Fig. 3 and pie charts in Fig. 1b and c. Cluster 2 had a large contribution from three populations from northern Xinjiang in the northwest of China (Ili, 0.526; Jin, 0.869 and Kuy, 0.577). Cluster 4 mainly consisted of individuals from two population from northeastern China and the two populations from western Europe (Don, 0.861; Mud, 0.960; Swi, 0.901; Ger, 0.963). Cluster 3 primarily contained individuals from the two populations from Gansu (Dun, 0.674 and Jiu, 0.726) and one population from the boundary region between Gansu and Xinjiang (Kum, 0.540). Cluster 1 was mostly made-up of
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Table 3 Analysis of molecular variance of populations from Xinjiang, Gansu, Heilongjiang and other countries Source of variation
Among groups Among populations within groups Within populations
d.f.
Sum of squares
Variance components
Percentage of variation
P value
Fixation indices
3
155.756
0.23546Va
9.64
P \ 0.0001
FCT = 0.09644
10
165.522
0.30390Vb
12.45
P \ 0.0001
FSC = 0.13776
666
1,266.820
1.90213Vc
77.91
P \ 0.001
FST = 0.22091
samples from the two populations of southern Xinjiang (Kor, 0.760 and Kas, 0.655) and one population from Gansu (Zha, 0.805). The Urumqi population contributed almost equally to cluster 1 and cluster 3, with cluster 1 being 0.412 and cluster 3 being 0.394; this presents a unique genetic structure. AMOVA results (Table 3) indicated that the major portion of the molecular genetic variation was found within populations (77.91 %) with 9.64 % among the four groups and 12.45 % among the populations within groups. Exact tests showed significant genetic variance on all of these three levels. The values of pairwise FST ranged from 0.040 to 0.337 (Table 4, ‘‘Appendix’’). Sixty-eight comparisons out of ninety-one showed high or very high genetic differentiation. The pairwise FST values among Ili, Jin and Kuy populations from the north of Xinjiang were less than 0.10, while other pairwise FST values were above 0.10 for populations from Xinjiang. The pairwise FST values between the populations from the northeast (Heilongjiang) and northwest (Xinjiang and Gansu) of China ranged from 0.157 to 0.324, indicating high or very high genetic differentiation (Wright 1978). All P values remained significant (P \ 0.05) after Bonferroni correction. The Mantel test for 10 populations from northwestern China revealed no correlation between genetic distances and geographic distances (r = 0.164, P = 0.176). However, the test showed a strong isolation-by-distance effect present among populations (r = 0.399, P = 0.015) when we added the two populations from Heilongjiang.
Discussion By genotyping 12 populations from the main C. pomonella distribution regions in China, we have
documented successive losses of genetic diversity and a high level of genetic structure in these moth populations. Genetic variation among populations from different regions was significant, and we found no evidence for a bottleneck effect. Genetic diversity in the Cydia pomonella population from Ili was highest among the populations from northwestern China. The more eastern populations of Kum, Dun, Jiu and Zha, and the more southern populations of Kor and Kas showed relatively lower genetic diversity. Gradual loss of genetic diversity was observed in other insect species (Ciosi et al. 2008; Margaritopoulos et al. 2009; Watts et al. 2010). For invasive species, genetic diversity is expected to decrease with range expansion and colonization of new areas via sequential founder populations or bottleneck effects (Ramachandran et al. 2005; Herborg et al. 2007; Dlugosch and Parker 2008). However, the BOTTLENECK analysis revealed no clear genetic evidence for a bottleneck effect in any of the studied populations. Lack of a bottleneck effect may be attributed to the recent invasion history and successful colonisation by a large numbers of migrants (Cham 2002; Parr 2005). Comparison of genetic variations between old and newly-introduced populations can provide a useful clue for deducing the origins of an introduced population or earliest founder population (Dlugosch and Parker 2008). The high level of genetic diversity in the Ili population together with the strategic location of Ili which is situated close to Horgost, the important land port to central Asia, both suggest that the Ili region is most likely the origin of codling moth introduction into northwestern China. The Ili River valley, with its extensive apple orchards provides a suitable habitat for successful invasion of codling moth. However, two populations (Kas and Kor) from southern Xinjiang had low genetic diversity. This
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result can be explained as populations expressing a fraction of the genetic variation that occurred in the source populations (Nei et al. 1975). A similar situation was also observed in populations from Kumul, Dunhuang, Jiuquan and Zhangye. Their genotypes are displaced and mixed through active or passive dispersal of individuals during the course of invasion. Selection of beneficial combinations related to local microclimatic and ecological conditions then improves local fitness and leads to new infestations (Lee 2002). As a result of multiple introduction events from distinct genetic sources, the newly-introduced populations often have higher genetic diversity than native or older populations (Novak and Mack 1993; Roman and Darling 2007; Dlugosch and Parker 2008; Brown and Stepien 2009; Gillis et al. 2009; Ghabooli et al. 2011). Since Cydia pomonella was reported in the northeast of China only 6 years ago, the two populations from Heilongjiang showing the highest genetic diversities among the studied populations could be explained by the new invasion event drawing from different genetic resources. Heilongjiang is around 3,000 km and 5,000 km away from Gansu and Xinjiang, respectively. In spite of the considerable distance from Heilongjiang to Xinjiang and Gansu, no occurrence of this pest has been recorded in the large area between them despite continuous monitoring. Taking together the higher genetic diversity and the distinct genetic structure revealed by the cluster analysis, we deduce that C. pomonella were not established by populations invading from western China. However, the Urumqi moths, as a relatively older population in China, showed a high level of genetic diversity and distinct structure. As the capital of Xinjiang, Urumqi could provide a corridor for accidental human-aided dispersal of C. pomonella, resulting in the intermixing of populations from northern and southern Xinjiang after years of successful invasion. The codling moth populations from Germany and Switzerland displayed a higher level of genetic diversity compared with populations from northwestern China. This aligns with the codling moth having a relatively shorter history in China than in southeastern Europe, the moth’s documented origin (Shel‘Deshova 1967). There was strong genetic structuring between populations based on the Bayesian clustering approach, AMOVA and pairwise FST. Previous studies
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of genetic variation showed that Cydia pomonella populations exhibited distinct characteristics of genetic structure in different habits, and that this is governed by a number of factors. Fuentes-Contreras et al. (2008) reported the genetic variation in central Chile and concluded that the low level of genetic differentiation observed was due to the adjacent applegrowing areas, the domestic culturing of fruits, the recent spread of modern fruit culture and the recent introduction of C. pomonella. Chen and Dorn (2009) analyzed genetic variation of codling moth populations in Switzerland and found that host specificity, geographic isolation, intrinsic flight capacity and anthropogenic measures could shape the population structure. Franck and Timm (2010) concluded that the population genetic structure of codling moths is primarily affected by hosts, geography and time. In the present study, the results of the Mantel test indicated that the pattern of variation was weakly associated with geographic distance among populations from northwestern China. Considering that the history of the codling moth in China spans only a little more than 50 years, this pattern may be the result of non-equilibrium from migration and drift, and due to the recent range expansion associated with human intervention (Slatkin 1993). The population from Zhangye had a very similar genetic structure to the two populations from southern Xinjiang. As the samples of Zhangye were collected in an apple and pear breeding center, where the famous Korla pear, Pyrus sinkiangensis Yu, has been frequently exported from Korla over the last 10 years, it is very possible that the similarity in genetic structure is due to human activity associated with the movement of nursery material. Human intervention in the form of fruit and seedling transport as well as the movement of bins has played an important role in the mixing of populations from distant geographic regions (Higbee et al. 2001). Similar circumstances were conjectured in northern Italy, Grapholita molesta populations (Torriani et al. 2010) and for codling moth populations from South Africa (Timm et al. 2006), which were genetically little differentiated despite the large geographic distance ([150 km) between some of the sampled orchards. Furthermore, C. pomonella has a limited dispersal ability, which would be expected to weaken IBD relationship over wide regions (Roff 1994). C. pomonella is generally regarded to be a sedentary species, with a sphere of activity extending over 0.5 ha
Genetic structure and diversity of a newly invasive species
(Mani and Wildbolz 1977). The weak flight capacity of the codling moth influences its dispersal and gene flow pattern (Timm et al. 2006; Chen and Dorn 2009). Significant differentiation between codling moth populations occurred across small geographic distances (&1 km) in South Africa (Timm et al. 2006) as well as in Switzerland (\10 km) (Chen and Dorn 2009). Moreover, the distribution areas of codling moth in China occur across very complex topography (huge mountains, large unpopulated areas or large deserts), different climates (temperate, continental arid or frigid climate) and different agricultural landscapes. A high level of population differentiation results from the high potential for adaptation to different environmental conditions (Carde´ and Minks 1995) and high reproductive rates (Myers et al. 2006), which allow these moths to form locally differentiated populations. Their weak flight capacity and humanaided dispersal increase this differentiation. The Bayesian clustering analysis indicated that populations from Ili, Jinghe and Kuytun had a similar proportion of membership coefficients and contributed mainly to cluster 3. Pairwise FST also showed relatively low values in these three populations. This result linking to relatively higher population genetic diversity estimates for the three populations suggests a novel finding where the Ili, Jinghe and Kuytun populations were established by individuals belonging to the same maternal lineage. In northern Xinjiang, two factors may have contributed to this result: the relative proximity of the fruit production regions and the frequent commodities exchange among these locations. This corresponds with indirect evidence obtained in South Africa using AFLPs and finding limited gene flow among C. pomonella populations among local regions due to isolated fruit production areas (Timm et al. 2006). The level of genetic differentiation between populations from northeastern and northwestern China was great. The two populations from Dongning and Mudanjiang contributed mainly to the same cluster 3. There was also strong isolation based on distance. These results add support to the view that the invasion
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of the codling moth into China came from at least two different pathways. We also found the populations from Heilongjiang had a similar genetic structure with those of western European populations. A phylogenetic study based on a greater number of collection sites is necessary to clarify the relationship among Cydia pomonella populations from European countries before precise conclusions can be drawn, especially the phylogenetic relationships of populations from western Europe and the far eastern region of Russia. The latter is near Heilongjiang and has been documented as a distribution region of codling moth (Willett et al. 2009). In China, management practices against Cydia pomonella have primarily focused on chemical control. Investigation of genetic diversity of C. pomonella populations can provide us a guide for controlling this pest. For example, high inbreeding and low genetic diversity leads to application of pesticides ultimately generating genetic bottlenecks. Furthermore, localized populations with similar genetic structure should be considered the same management unit for most effective control (Ayres et al. 2010). For isolated populations, we should try various management methods, especially a variety of different chemical pesticides. In many cases, immigrating individuals from isolated orchards may result in the sequential introduction of novel resistance genes and promote pesticide resistance in their gene pool (Bouvier et al. 2001). Additional research needs to be done to detail the geographic origins of codling moth in China and its spread through China. Acknowledgments We thank Dr. John Richard Schrock, Emporia State University, Emporia, KS, USA, for language correction of the draft. We are grateful to Anyong Wang, Kun Dong, Chunhan Zheng, Jianqiang Yang, Xiao Zhao and Xinglong Huang for their help in the collection of codling moth samples. This work was supported by grants from the ‘13115’ Sci-Tech Innovation Project of Shaanxi Province (No. 2009ZDKG-06), the Special Fund for Agro-scientific Research in the Public Interest (No. 200903042-03), the National Natural Science Foundation of China (No. 31071687), the International Atomic Energy Agency (No. 16341) and the Talent Recruitment Project of Northwest A & F University.
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Q.-L. Men et al.
Appendix See Table 4. Table 4 Pairwise population differentiation estimates (FST) averaging eight loci between all populations of C. pomonella (below the diagonal) and P values (above diagonal) Zha Zha
Jiu
Dun
Uru
Kum
Kas
Jin
Kor
Ili
Kuy
Mud
Don
Ger
Swi
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
Jiu
0.182
Dun
0.100
0.195
Uru
0.116
0.155
0.146
Kum Kas
0.152 0.144
0.121 0.272
0.142 0.236
0.173 0.205
0.166
Jin
0.244
0.252
0.274
0.209
0.133
0.246
Kor
0.096
0.337
0.306
0.223
0.323
0.332
0.278
Ili
0.154
0.185
0.120
0.124
0.110
0.105
0.083
0.203
Kuy
0.196
0.105
0.199
0.159
0.112
0.210
0.098
0.277
0.080
Mud
0.292
0.303
0.294
0.194
0.316
0.324
0.244
0.231
0.187
0.217
Don
0.277
0.264
0.288
0.204
0.268
0.301
0.196
0.241
0.157
0.161
0.112
Ger
0.264
0.298
0.282
0.154
0.300
0.301
0.214
0.220
0.158
0.198
0.060
0.088
Swi
0.266
0.264
0.266
0.159
0.281
0.289
0.222
0.223
0.157
0.176
0.040
0.110
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
** **
** **
** **
** **
** **
** **
** **
** **
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
** *
0.043
* P \ 0.05, ** P \ 0.01; The significances were tested for multi comparisons by the Boferroni method for k = 91, P \ 0.05
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