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. 2021 Apr 12;21(2):14. doi: 10.1093/jisesa/ieaa151

CAP Analysis of the Distribution of the Introduced Bemisia tabaci (Hemiptera: Aleyrodidae) Species Complex in Xinjiang, China and the Southerly Expansion of the Mediterranean Species

Zunzun Jia 1,2, Kaiyun Fu 2,2, Wenchao Guo 3,✉, Weihua Jiang 4, Tursun Ahmat 2, Xinhua Ding 2, Jiang He 2, Xiaowu Wang 3
Editor: Xiao-Wei Wang
PMCID: PMC8040787  PMID: 33844016

Abstract

Bemisia tabaci (Gennadius) cryptic complex has invaded Xinjiang, China, since 1998. The distribution of Mediterranean (MED) and Middle East-Asia Minor 1 (MEAM1) B. tabaci substrains has been gradually identified due to the development of molecular technology. In this study, the distribution of MED and MEAM1 in Xinjiang was determined by cleaved amplified polymorphic sequence (CAPs). Results showed that MED dominated in northern Xinjiang (84%), whereas MEAM1 was dominant in southern Xinjiang (72%). Five pairs of simple sequence repeat (SSR) primers were used to analyze the genetic diversity of B. tabaci among 36 geographic populations. The genetic diversity of MED and MEAM1was low and varied little among populations in Xinjiang (0.09 ± 0.14 and 0.09 ± 0.13, respectively). Based on ∆K statistic, 13 populations of MEAM1 could be classified into two subgroups at K = 2, whereas the 23 populations of MED could be classified into four subgroups at K = 4. However, Mantel t-test demonstrated no correlation between geographical and genetic distances among B. tabaci complex (R = 0.42, P = 1.00). Neighbor-joining and principal coordinate analysis showed that geographical isolation and interspecific differences were the main causes of the genetic variation. Gene flow predicted that MEAM1 was most likely introduced from Urumqi to the southern Xinjiang. Meanwhile, a large proportion of MED in Kashi region came from Changji and Yining. To block ongoing dispersal, strict detection and flower quarantine regulations need to be enforced.

Keywords: Bemisia tabaci, cryptic species, invasive species, genetic diversity

The whitefly, Bemisia tabaci (Gennadius), is a rapidly evolving cryptic species complex (Perring 2001), composed of at least 34 cryptic species (Boykin and De Barro 2012, Alemandri et al. 2015, Götz and Winter 2016). Among the complex members, Middle East-Asia Minor 1 (MEAM1, previously known as B biotype) and Mediterranean (MED, previously known as Q biotype) are the most adaptive whitefly cryptic species in the world. Both cause severe damage to feeding crops and horticultural plants, e.g., nectar secretion leading to sooty mold, and transmission of various viruses (Judith and Czosnek 2002, Paul and Barro 2012, Navas-Castillo 2014, Gilbertson et al. 2015, Wainaina et al. 2018), including cotton leaf curl Multan virus (CLCuMuV), which causes a significant reduction in yield (Nur and Abu Salih 1970, Briddon and Markham 2000, Tahir et al. 2011, Chatzivassiliou et al. 2007).

As an important invasive pest, B. tabaci first invaded China in 1949 (Zhou 1949). In the 1990s, B. tabaci brought serious damage to various regions (Zhang 2000, Chu et al. 2010, Wang et al. 2010, Guo et al. 2014). In 1998, B. tabaci first settled in the flower market in Urumqi, and it soon spread to Shihezi, Hami, Korla, Karamay, and Turpan (Zhao et al. 2000). Before 2010, MEAM1 was the sole species identified in Xinjiang (Zhao et al. 2000). Using sequence characterized amplified regions (SCAR) and COI (cytochrome oxidase I) barcoding, Cao et al. (2011) found out that MED had invaded Xinjiang.

Recently, molecular markers such as COI barcoding and simple sequence repeat (SSR) have been widely used to identify species of whiteflies. These tools help in understanding genetic diversity, gene flow, and even the replacement of populations (David 1998, Götz and Winter 2016). Boykin et al. (2007) used the whole COI sequence to analyze the genetic diversity of the B. tabaci, and he unveiled 12 members of the species complex. With the discovery of MED B. tabaci’s invasion, it was then known that Xinjiang harbored at least two cryptic species: MED and MEAM1 (Duan et al. 2011).

SSR is widely used in identification and in investigations related to the genetic evolution, genetic mapping, phylogenetic, and genetic diversity of animal and plant species. For example, Colorado potato beetles, Leptinotarsa decemlineata (Say) (Coleoptera: chrysomelidae), was a notoriously invasive species. Zhang et al. (2013) analyzed its genetic diversity among 10 geographical populations in Xinjiang and the mode of the beetle diffusion from the border to inner Xinjiang. Previous report on B. tabaci showed that SSR may not distinguish cryptic species in the complex unless there was an appropriate method to study population structure and the mechanism of invasion and diffusion (Chu et al. 2012). Thus, cleaved amplified polymorphic sequence (CAPs) was used to detect cryptic species within the B. tabaci species complex. Previous COI barcoding studies showed that only MEAM1 and MED of B. tabaci were found in Xingjiang (Ashfaq et al. 2015).

With the expansion of greenhouses and the increasing frequency of flower trade, whiteflies have invaded most areas in Xinjiang (Jia et al. 2017). As the largest cotton-producing area in China, Xinjiang is facing great threats of the whitefly-transmitted cotton leaf curl Multan virus. Despite of this, the distribution of MED and MEAM1 B. tabaci remains unclear. Meanwhile, the ecological conditions of the unique desert oasis agriculture in Xinjiang may impose certain limitations on the spread of whitefly. In this study, we aimed to uncover the distribution patterns of MEAM1 and MED from 2015 to 2017 to provide insights into the invasion and diffusion of the B. tabaci complex in Xinjiang.

Materials and Methods

Samples

Bemisia tabaci samples were collected from 54 sites in Xinjiang from June 2015 to December 2017 (Table 1). At least 200 adults per site were collected, placed in tubes containing 95% ethanol, and stored at −20°C. Thirty to 50 samples were randomly selected for identification. In total, 1,768 adults were identified as MEAM1 or MED cryptic species. We chose samples for SSR by initially, randomly sampling the sites, and then by the individuals coming from the chosen sites (Table 1).

Table 1.

Sampling information for Bemisia tabacicollected in Xinjiang

Area No. Population Time Longitude and latitude Host No. No. of SSR
Urumqi 1 Urumqi-1 2016.5 (43°48′49″N, 87°34′37″W) Pharbitis nil 28 0
2 Urumqi-2 2016.8 (43°48′21″N, 87°33′22″W) Cucumissativus 50 49
Subtotal 78 49
Karamay 3 Karamay 2016.5 (45°29′26″N, 84°57′23″W) Solanum lycopersicum 50 40
Hami City 4 Hami-1 2016.4 (42°50′32″N, 93°27′18″W) Solanum lycopersicum 30 16
5 Hami-2 2016.4 (43°0′35″N, 93°35′58″W) Solanum lycopersicum 30 24
Subtotal 60 40
Changji hui autonomous prefecture 6 Changji-1 2016.5 (44°0′40″N, 87°18′29″W) Salvias plendens 60 41
7 Changji-2 2017.3 (44°9′32″N, 88°1′22″W) Hibiscus rosa-sinensis 15 0
8 Changji-3 2017.3 (44°9′32″N, 87°58′45″W) Hibiscus rosa-sinensis 15 0
Subtotal 90 41
Bortala Mongol Autonomous Prefecture 9 Alashankou 2016.9 (45°10′12″N, 82°33′36″W) Hibiscus rosa-sinensis 50 50
Mongolian Autonomous 10 Bazhou-1 2015.8 (38°7′4″N, 85°34′46″W) Vignaunguiculata 30 28
Prefecture of Bayingolin 11 Bazhou-2 2015.8 (38°8′3″N, 85°30′42″W) Vignaunguiculata 30 0
12 Bazhou-3 2015.8 (38°6′10″N, 85°31′9″W) Cucurbita moschata 30 19
13 Korla 2016.3 (41°46′12″N, 86°8′57″W) Hibiscus rosa-sinensis 30 0
Subtotal 120 47
AksuPrefecture 14 Aksu 2017.3 (40°32′19″N,81°17′53″W) Hibiscus rosa-sinensis 30 0
Kashgar Prefecture 15 Kashi-1 2015.9 (38°24′44″N, 77°17′3″W) Gossypiumspp 50 33
16 Kashi-2 2015.7 (39°24′0″N, 75°54′0″W) Solanum lycopersicum 30 11
17 Kashi-3 2015.8 (38°21′22″N, 77°10′56″W) Gossypiumspp 30 11
18 Kashi-4 2015.8 (38°17′9″N, 77°11′49″W) Gossypiumspp 30 6
19 Kashi-5 2015.8 (38°19′20″N, 77°12′10″W) Gossypiumspp 30 9
20 Kashi-6 2015.8 (38°20′44″N, 77°09′26″W) Gossypiumspp 30 4
21 Kashi-7 2015.9 (39°24′35″N, 76°8′1″W) solanum melongena 30 5
22 Kashi-8 2015.9 (39°24′21″N, 76°5′3″W) solanum melongena 30 10
23 Kashi-9 2015.9 (39°14′3″N, 76°22′35″W) Vignaunguiculata 30 0
24 Kashi-10 2015.9 (38°55′41″N, 76°8′13″W) Cucumissativus 30 0
25 Kashi-11 2015.9 (39°47′0″N, 78°31′46″W) solanum melongena 30 0
26 Kashi-12 2015.9 (39°47′41″N, 78°34′18″W) Cucumissativus 30 0
27 Kashi-13 2016.4 (38°18′28″N, 77°56′42″W) Solanum lycopersicum 30 0
Subtotal 410 89
Hotan Prefecture 28 Hetian-1 2015.6 (37°27′47″N, 79°52′29″W) Solanum lycopersicum 60 0
29 Hetian-2 2015.8 (37°4′4″N, 82°42′55″W) Vignaunguiculata 30 10
30 Hetian-3 2015.8 (37°4′6″N, 82°40′11″W) Canavaliagladiata 30 0
31 Hetian-4 2015.8 (37°3′29″N, 82°41′41″W) Solanum lycopersicum 30 6
32 Hetian-5 2015.8 (36°50′16″N, 81°38′51″W) Solanum lycopersicum 30 14
33 Hetian-6 2014.8 (36°49′1″N, 81°38′23″W) Solanum lycopersicum 30 5
34 Hetian-7 2015.8 (36°50′58″N, 81°37′8″W) solanum melongena 30 0
35 Hetian-8 2015.8 (36°59′55″N, 81°4′12″W) Capsicum annuum 30 6
36 Hetian-9 2015.8 (36°59′49″N, 80°58′51″W) Brassica rapa 30 12
37 Hetian-10 2015.8 (37°14′14″N, 79°41′34″W) Brassica rapa 30 0
38 Hetian-11 2015.8 (37°14′18″N, 79°44′19″W) Solanum lycopersicum 30 5
39 Hetian-12 2015.8 (37°17′52″N, 79°39′45″W) Cucurbita moschata 30 5
40 Hetian-13 2016.4 (37°15′7″N, 80°5′12″W) Solanum lycopersicum 60 27
Subtotal 450 90
Ili Kazak Autonomous Prefecture 41 Yining-2015 2015.5 (43°51′28″N, 81°25′56″W) Capsicum annuum 30 0
42 Yining-2016 2016.3 (43°51′29″N, 81°25′56″W) Capsicum annuum 50 50
Subtotal 80 50
Turpan 43 Tulupan-1 2015.6 (42°59′59″N, 89°9′58″W) Solanum lycopersicum 30 0
44 Tulupan-2 2015.6 (42°57′56″N, 89°6′16″W) Cucumber 30 10
45 Tulupan-3 2015.6 (42°48′40″N, 88°27′17″W) Gossypiumspp 30
46 Tulupan-4 2016.4 (42°46′54″N, 88°41′56″W) Solanum lycopersicum 30 13
47 Tulupan-5 2016.4 (42°79′37″N, 88°38′24″W) Solanum lycopersicum 30 18
48 Tulupan-6 2016.4 (42°57′6″N, 89°5′22″W) Solanum lycopersicum 30 14
49 Tulupan-7 2016.4 (42°46′39″N, 89°33′31″W) Solanum lycopersicum 30 18
50 Tulupan-8 2016.4. (42°47′14″N, 89°43′18″W) Solanum lycopersicum 30 11
51 Tulupan-9 2016.4 (42°43′21″N, 89°41′43″W) Pharbitis nil 30 8
Subtotal 270 92
Shihez 53 Shihezi 2016.5 (44°18′22″N, 86°4′48″W) Euphorbia pulcherrima 50 43
Alaer 54 Alaer 2017.3 (40°32′42″N, 81°16′41″W) Hibiscus rosa-sinensis 30 0
Total 1,768 631
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Cleaved Amplified Polymorphic Sequence

Total genomic DNA was extracted from each adult according to Frohlich et al. (2010). Briefly, each adult was laid on clean parafilm and homogenized in DNase-free Eppendorf tube (Thermo Fisher Scientific) containing 5-µl lysis buffer (5 mM Tris [pH 8.0], 0.5 mM EDTA, 0.5% NP40, and 1 mg/ml protein K+; Beijing Solarbio Science & Technology Co., Ltd.). The homogenate was then transferred to tubes with 30-µl lysis buffer and incubated at 65°C for 15 min and then at 95°C for 10 min. The products were stored at −20°C.

A 13-µl reaction system containing 2.6-µl DNA, 6.5-µl 2× Taq PCR Master Mix (Tiangen Biotech Co., Ltd. Beijing), and 0.52-µl primer (20 µM, Nanjing Jinsirui Biotechnology Co., Ltd.) was predenaturated at 95°C for 5 min, followed by 40 cycles of denaturation at 95°C for 30 s. After annealing at 52°C for 30 s and extension at 72°C for 30 s, samples went through a final extension at 72°C for 5 min. All PCR products were stored at −20°C. The forward and reverse primers were C1-J-2195 (5′-TTGATTTTTTGGTCATCCAGAAGT-3′) and R-BQ-2819 (5′-CTGA-ATATCGRCGAGGCATTCC-3′), respectively (Simon et al. 1994, Chu 2010).

Enzyme digestion method followed Horowitz et al. (2005) and Chu (2010). Seven microliters of VspI Digestion Mix (0.5 µl VspI, 4.5 µl H2O, 2 µl buffer 0; Thermo Fisher Scientific) was added to each of the 20-µl PCR products, and the final mixture was incubated at 37°C for 2 h. Digestion products were electrophoresed on 1% agarose gel under 120 V for 45 min and visually inspected under BIO-RAD Gel Doc XR+. MEAM1 individuals were identified by the presence of a unique 620-bp band, whereas MED individuals exhibited 498- and 122-bp bands.

Simple Sequence Repeat

Five microsatellite markers were selected according to previous studies of De Barro et al. (2003) and Valle et al. (2012; Table 2). The 25-µl PCR system contained 2-µl DNA, 12.5 µl 2× Taq PCR MaserMix (Tiangen Biotech [Beijing] Co., Ltd.), and 1 µl of each primer (20 µM). The PCR program was set to the following conditions: initiation at 95°C for 5 min, followed by 45 cycles of denaturation at 95°C for 30 s, annealing for 30 s, extension at 72°C for 30 s, then final extension at 72°C for 5 min. Annealing temperatures for each primer are provided in Table 2. The PCR products were analyzed by capillary electrophoresis following the directions of DNF-905 kiton Fragment Analyzer TM (Advanced Analytic Service). Fragment detailed data were extracted using Agilent PROSize 2.0.

Table 2.

The information of simple sequence repeat molecular marker primer

Microsatellite locus Direction Primer sequences Annealing temperature (°C) References
bem31 F GTCATTTCTGGATTCTCAGCA 57 De Barro et al. (2003)
R AAGAACTAGCCAGGGACAAAC
bem40 F GAAAGTGGAGAGTTTAGGTGA 57 De Barro et al. (2003)
R TGGAGAATGTTATAAAGTGGA
BEM06 F GATGGCTTATGTTATAATACTA 52 De Barro et al. (2003)
R TTACACTTAACACCAGAACT
bta4 F CGGCAGTCAGGGTTATT 56 Valle et al. (2012)
R CGCTCCTCAAGTTTTCTGT
bta5 F GCGTAGGAGAGTTGGAATGC 60 Valle et al. (2012)
R TATACTTGGGCATCGTCAGC
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Genetic Diversity

PopGene32 was used to analyze genetic diversity parameters such as the number of alleles (Na), number of effective alleles (Ne), average diversity index (H), and the Shannon diversity index (I). The inbreeding coefficients (FIS) were also calculated to reflect the inbreeding status of the populations. Genetic differences among populations were evaluated by pairwise FIS values, with 10 000 permutations to assess significance (Weir and Cockerham 1984; FSTAT v.2.9.3.2, Goudet 2002).

Population Relationships

Group and population clusters were generated using STRUCTURE v.2.3.3 based on Bayesian methods (STRUCTURE v.2.3.3, Pritchard et al. 2000). ΔK method was used to estimate the optimum number of genetic groups (Evanno et al. 2005). We chose the admixture model with correlated allele frequencies and set the K from 1 to 10 for MED group and from 1 to 20 for MEAM1 group. We ran three replicates of each run and set a burn-in period of 50 000 Markov chain Monte Carlo generations followed by 5 × 105 iterations. To confirm the existence of population structure, analysis of molecular variance (AMOVA) was performed to assess the genetic variance partitioned into four levels (among groups, among populations, within groups, within populations, and within individuals) based on structure output, with 10 000 permutations to test for significance (Arlequin v.3.11, Excoffier et al. 2005). Correlation between geographical and genetic distances (Slatkin’s linear FST, FST/(1 − FST), and D′) of all samples from Xinjiang as well as between and within groups were defined by Structure (Mantel procedure, GenAlEx 6.2; Peakall and Smouse 2006). We used a neighbor-joining cluster method in MEGA 3.01 to generate a circle phylogenetic tree based on D′ distance which was calculated using Popgene32. To assess the genetic associations of individuals among various groups representing cryptic species and population, a pairwise similarity matrix was generated using simple matching coefficient (Sokal and Michener 1958), with 10 000 permutations. Random, this was converted to Euclidean distance matrix as the square root of 1 minus element-wise similarity for a principal coordinate analysis (PCoA) using NTSYS-pc 2.01 (Rohlf 1987). Top 2 components were plotted for MED, MEAM1, MED, and MEAM1 complex. Direction of gene flow was inferred by a partial Bayesian method from GENECLASS 2.0, and assignment probabilities of individuals were tested from geographical populations with a threshold probability value of 0.01. Assigned probability data of MED to MED, MED to MEAM1, MEAM1 to MED, MEAM1 to MEAM1 groups were arcsin-square root transformed to normal distribution. Significant levels (P < 0.05) were tested using Tukey’s multiple comparisons test of ANOVA in Graphpad Prism 6.0 (Rannala and Mountain 1997) and GeneClass v.2.0 (Paetkau et al. 2004, Piry et al. 2004).

Results

The Distribution of B. tabaci Cryptic Species

The distribution of B. tabaci cryptic species were identified in 54 sampling sites during 2015 and 2017 by CAPs. The randomly chosen 631 samples for SSR were also sequenced to verify these results (data not shown). Results showed that there was a large percentage difference between northern and southern Xinjiang (Fig. 1). The MED was the dominant cryptic species in northern Xinjiang, accounting for 84% of the samples. Apart for Urumqi (26%), it dominated in Changji Hui Autonomous Prefecture (87%), Shihezi City (100%), Karamay City (100%), Yili District (100%), Bortala Mongolian Autonomous Prefecture (100%), Turpan Region (100%), and Hami Region (97%). Meanwhile, the MEAM1 was the dominant cryptic species in southern Xinjiang, accounting for 73% of the samples. It dominated in Hotan (95%), Bazhou (91%), Kashgar (38%), and Aksu (100%). No species were present in Alar (0%; Fig. 2).

Fig. 1.

Fig. 1.

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Sampling for Bemisia tabaci collected in Xinjiang.

Fig. 2.

Fig. 2.

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Distribution of 54 populations of Bemisia tabaci cryptic species in Xinjiang. Sampling sites: 1. Hetian Prefecture; 2. Kashgar Prefecture; 3. Mongolian Autonomous Prefecture of Bayingolin; 4. Alaer; 5. Aksu Prefecture; 6. Hami City; 7. Turpan; 8. Bortala Mongol Autonomous Prefecture; 9. Ili Kazak Autonomous Prefecture; 10. Karamay; 11. Shihez; 12. Changji hui autonomous prefecture; 13. Urumqi.

Genetic Diversity

Five pairs of SSR primers were used to analyze the genetic diversity of 631 samples belonging to 36 randomly selected B. tabaci populations (Table 1). Polymorphism position and polymorphism information content, ranging from 15 to 48 and 0.92 to 0.95, respectively, showed high loci polymorphism in each population. The results also showed that the primers used were applicable for genetic diversity analysis of B. tabaci. The MEAM1 and MED population’s diversity indices including number of alleles per locus (na), effective number of alleles (ne), gene diversity (h), and Shannon’s Information index (I) averaged at 1.77 ± 0.42 and 1.92 ± 0.27 (range 1.15–1.48), 1.13 ± 0.25 and 1.13 ± 0.25 (range 1.07–1.13), 0.09 ± 0.14 and 0.09 ± 0.13 (range from 0.04 to 0.08), and 0.15 ± 0.20 and 0.15 ± 0.20 (range 0.07–0.13), respectively. These indicates low genetic diversity and little variation among populations (Table 2). Fis values were all negative in 36 SSR analyzed sites with 12 and 20 populations reaching P < 0.05 and P < 0.001 significant departure levels. These results indicate that the genetic diversity of B. tabaci in Xinjiang is low and the genetic differentiation is high.

Genetic Relationship

∆K Statistic

Based on ∆K statistic, 13 populations of MEAM1 could be divided into two subgroups at K = 2, whereas the 23 populations of MED could be divided into four subgroups at K = 4 (Table 3). In MEAM1, one group Kashi-1, Kashi-2, Kashi-3 included those from Tulupan; the third group included those from Changji and Yining; and finally, the fourth group included those from Alashankou, Shihezi, and Hami.

Table 3.

Proportion of membership of each predefined population in MEAM1 or MED groups

Population K = 2, P = 13 Population K = 4, P = 23
MEAM1 1 2 MED 1 2 3 4
Urumqi-2-MEAM1 0.25 0.75 Kelamayi-MED 0.05 0.02 0.88 0.05
Hetian-13-MEAM1 0.04 0.96 Urumqi-2-MED 0.32 0.02 0.57 0.09
Hetian-2-MEAM1 0.05 0.96 Kashi-2-MED 0.03 0.08 0.85 0.04
Hetian-4-MEAM1 0.04 0.96 Kashi-1-MED 0.03 0.03 0.90 0.05
Hetian-5-MEAM1 0.03 0.97 Kashi-3-MED 0.03 0.02 0.93 0.01
Hetian-6-MEAM1 0.04 0.96 Kashi-4-MED 0.02 0.03 0.94 0.02
Hetian-8-MEAM1 0.02 0.98 Kashi-5-MED 0.08 0.06 0.76 0.1
Hetian-9-MEAM1 0.07 0.93 Kashi-6-MED 0.01 0.24 0.74 0.01
Hetian-11-MEAM1 0.12 0.88 Kashi-7-MED 0.02 0.02 0.95 0.01
Hetian-12-MEAM1 0.03 0.97 Hetian-13-MED 0.04 0.02 0.92 0.02
Kashi-8-MEAM1 0.14 0.86 Tulupan-2-MED 0.64 0.17 0.02 0.18
Bazhou-1-MEAM1 0.96 0.04 Tulupan-4-MED 0.65 0.12 0.14 0.1
Bazhou-3-MEAM1 0.96 0.04 Tulupan-5-MED 0.8 0.04 0.08 0.09
Tulupan-6-MED 0.63 0.06 0.11 0.2
Tulupan-7-MED 0.89 0.05 0.03 0.03
Tulupan-8-MED 0.51 0.1 0.16 0.23
Tulupan-9-MED 0.45 0.1 0.05 0.4
Changji-MED 0.11 0.74 0.1 0.04
Yining-MED 0.14 0.75 0.02 0.08
Alashankou-MED 0.09 0.35 0.03 0.54
Shihezi-MED 0.14 0.39 0.02 0.45
Hami-1-MED 0.21 0.32 0.02 0.46
Hami-2-MED 0.11 0.13 0.01 0.74
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Bold indicate the proportion of each population assigned to the most reliable group. K, the number of inferred population groups; P, the number of populations.

Analysis of Molecular Variance

Basing on the subdivision by ΔK, AMOVA demonstrated that most of the variation came from within individuals (100.89%, 115.31%; Table 4), with K = 2 in MEAM1 group and K = 4 in MED group. Fixation indices of within populations (Fis) were negative (−0.23, −0.23). It demonstrated that there was a strong disassortative mating, which was consistent with the Fis in each population (Table 5). Variation among population and groups in MEAM1 and MED population was low but significant (df = 11, F = 0.06, P = 0.000; df = 19, F = 0.04, P = 0.00; df = 1, F = 0.12, P = 0.00, df = 3, F = 0.03, P = 0.00; Table 4), which supported the groupings.

Table 4.

Analysis of molecular variance

Source of variation df F P-value PV
MEAM1 MED MEAM1 MED MEAM1 MED MEAM1 MED
K = 2 K = 4 K = 2 K = 4 K = 2 K = 4 K = 2 K = 4
Among groups 1 3 0.12 0.03 0.02 0.00 12.38 3.06
Among populations 11 19 0.06 0.04 0.00 0.00 5.32 3.43
Within populations 160 434 −0.23 −0.23 1.00 1.00 −18.59 −21.79
Within individuals 173 457 −0.01 −0.15 1.00 1.00 100.89 115.31
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df, degree of freedom; K, the number of inferred population groups based on STRUCTURE analysis; F, fixation index; PV, percent variation; P-value, probability of the significance tests.

Table 5.

Summary data of genetic diversity among groups and populations in Xinjiang, China

Population N n a n e h I PP PIC F is P-value
All 631 2.00 ± 0.00 1.13 ± 0.25 0.09 ± 0.13 0.15 ± 0.19 99 0.96 —
MEAM1 174 1.77 ± 0.42 1.13 ± 0.25 0.09 ± 0.14 0.15 ± 0.20 76 0.96 —
MED 457 1.92 ± 0.27 1.13 ± 0.25 0.09 ± 0.13 0.15 ± 0.20 91 0.96 —
MEAM1
 Urumqi-2-MEAM1 33 1.45 ± 0.50 1.12 ± 0.24 0.08 ± 0.13 0.13 ± 0.13 45 0.95 −0.25 0.0003
 Bazhou-1-MEAM1 28 1.30 ± 0.46 1.09 ± 0.23 0.06 ± 0.13 0.09 ± 0.13 30 0.92 −0.24 0.0003
 Bazhou-3-MEAM1 19 1.27 ± 0.45 1.11 ± 0.25 0.07 ± 0.14 0.11 ± 0.14 27 0.93 −0.17 0.0053
 Kashi-8-MEAM1 10 1.26 ± 0.44 1.10 ± 0.23 0.06 ± 0.13 0.10 ± 0.13 26 0.93 −0.29 0.0003
 Hetian-13-MEAM1 21 1.38 ± 0.49 1.12 ± 0.25 0.08 ± 0.14 0.12 ± 0.14 38 0.94 −0.28 0.0003
 Hetian-2-MEAM1 10 1.28 ± 0.45 1.13 ± 0.28 0.08 ± 0.15 0.12 ± 0.15 28 0.94 −0.25 0.0008
 Hetian-4-MEAM1 6 1.19 ± 0.40 1.10 ± 0.24 0.06 ± 0.14 0.09 ± 0.14 19 0.93 −0.31 0.0003
 Hetian-5-MEAM1 14 1.24 ± 0.43 1.09 ± 0.23 0.06 ± 0.13 0.10 ± 0.13 24 0.93 −0.34 0.0003
 Hetian-6-MEAM1 5 1.21 ± 0.41 1.11 ± 0.25 0.07 ± 0.14 0.10 ± 0.14 21 0.93 −0.26 0.0047
 Hetian-8-MEAM1 6 1.15 ± 0.36 1.07 ± 0.21 0.04 ± 0.12 0.07 ± 0.12 15 0.92 −0.37 0.0006
 Hetian-9-MEAM1 12 1.29 ± 0.45 1.12 ± 0.25 0.07 ± 0.14 0.12 ± 0.14 29 0.94 −0.29 0.0003
 Hetian-11-MEAM1 5 1.27 ± 0.44 1.14 ± 0.24 0.08 ± 0.14 0.12 ± 0.21 27 0.94 −0.13 0.0869
 Hetian-12-MEAM1 5 1.22 ± 0.42 1.13 ± 0.23 0.06 ± 0.13 0.10 ± 0.20 22 0.93 −0.25 0.0025
MED
 Kelamayi-MED 40 1.48 ± 0.50 1.11 ± 0.24 0.07 ± 0.13 0.13 ± 0.13 48 0.94 −0.26 0.0003
 Urumqi-2-MED 16 1.36 ± 0.48 1.11 ± 0.22 0.07 ± 0.13 0.12 ± 0.13 36 0.95 −0.26 0.0003
 Changji-MED 41 1.47 ± 0.50 1.12 ± 0.25 0.08 ± 0.14 0.13 ± 0.14 47 0.95 −0.22 0.0003
 Yining-MED 50 1.38 ± 0.49 1.12 ± 0.26 0.08 ± 0.15 0.12 ± 0.15 38 0.94 −0.29 0.0003
 Alashankou-MED 50 1.42 ± 0.50 1.12 ± 0.25 0.08 ± 0.14 0.12 ± 0.14 42 0.94 −0.20 0.0003
 Shihezi-MED 43 1.45 ± 0.50 1.13 ± 0.26 0.08 ± 0.14 0.13 ± 0.14 45 0.95 −0.23 0.0003
 Hami-1-MED 16 1.31 ± 0.47 1.11 ± 0.23 0.07 ± 0.13 0.12 ± 0.20 31 0.95 −0.22 0.0003
 Hami-2-MED 24 1.39 ± 0.49 1.11 ± 0.23 0.07 ± 0.13 0.12 ± 0.13 39 0.95 −0.18 0.0003
 Tulupan-2-MED 10 1.25 ± 0.44 1.10 ± 0.23 0.07 ± 0.13 0.10 ± 0.13 25 0.94 −0.21 0.0025
 Tulupan-4-MED 13 1.28 ± 0.45 1.10 ± 0.23 0.07 ± 0.13 0.11 ± 0.13 28 0.94 −0.21 0.0003
 Tulupan-5-MED 18 1.40 ± 0.49 1.13 ± 0.25 0.08 ± 0.14 0.13 ± 0.14 39 0.95 −0.15 0.0017
 Tulupan-6-MED 14 1.31 ± 0.47 1.12 ± 0.25 0.07 ± 0.14 0.12 ± 0.14 31 0.95 −0.18 0.0031
 Tulupan-7-MED 18 1.35 ± 0.48 1.12 ± 0.25 0.08 ± 0.14 0.12 ± 0.14 35 0.95 −0.18 0.0003
 Tulupan-8-MED 11 1.24 ± 0.43 1.10 ± 0.24 0.06 ± 0.14 0.10 ± 0.14 24 0.94 −0.19 0.0097
 Tulupan-9-MED 8 1.29 ± 0.45 1.13 ± 0.26 0.08 ± 0.15 0.13 ± 0.15 29 0.95 −0.15 0.0361
 Kashi-2-MED 11 1.35 ± 0.48 1.12 ± 0.24 0.08 ± 0.14 0.13 ± 0.14 35 0.95 −0.19 0.0008
 Kashi-1-MED 33 1.47 ± 0.50 1.12 ± 0.23 0.08 ± 0.14 0.13 ± 0.14 47 0.95 −0.24 0.0003
 Kashi-3-MED 11 1.33 ± 0.47 1.11 ± 0.22 0.07 ± 0.13 0.12 ± 0.13 33 0.94 −0.25 0.0003
 Kashi-4-MED 6 1.28 ± 0.45 1.12 ± 0.24 0.08 ± 0.14 0.12 ± 0.14 28 0.95 −0.08 0.2736
 Kashi-5-MED 9 1.28 ± 0.45 1.09 ± 0.20 0.06 ± 0.12 0.11 ± 0.12 28 0.94 −0.16 0.0219
 Kashi-6-MED 4 1.21 ± 0.41 1.12 ± 0.26 0.07 ± 0.15 0.11 ± 0.15 21 0.94 −0.19 0.1000
 Kashi-7-MED 5 1.18 ± 0.39 1.10 ± 0.24 0.06 ± 0.14 0.09 ± 0.14 18 0.93 −0.31 0.0119
 Hetian-13-MED 6 1.18 ± 0.39 1.10 ± 0.24 0.06 ± 0.14 0.09 ± 0.14 45 0.95 −0.18 0.0433
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N, sample size; na, observed number of alleles; ne, effective number of alleles (Kimura and Crow 1964); h, Nei’s (1973) gene diversity; I, Shannon’s Information index (Lewontin 1972). Fis, inbreeding coefficient. PP, polymorphism position; PIC, polymorphism information content.

Mantel t-Test

Mantel test analysis of the matrix (Ln Km) based on ΔK subdivision showed that there was no correlation between geographical and genetic distances among B. tabaci complex (R = 0.42, P = 1.00), or in MED (R = 0.49, P = 1.00) and MEAM1 (R = 0.44, P = 1.00) group or subgroup (Table 6). MEAM1-group2 and MED-group3’s data were not available as there were only two populations in these groups.

Table 6.

The correlations between genetic distance and geographic distance among Bemisia tabaci populations in Xinjiang, China

All MEAM1 group MED group
K = 2 K = 4
All Subgroup 1 Subgroup 2 All Subgroup 1 Subgroup 2 Subgroup 3 Subgroup 4
R 0.42 0.44 0.42 Naa 0.49 0.20 −0.22 Na 0.05
P 1.00 1.00 1.88 Na 1.00 0.78 −1.22 Na 0.50
T 9.82 3.10 0.97 Na 5.60 0.78 0.11 Na 0.69
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R, matrix correlation, P, prob. random Z < obs. Z; T, approximate Mantel t-test.

a Na, not available because there was only two populations in this group.

Neighbor-Joining Tree

The neighbor-joining tree (Fig. 3A) showed that most of the MED and MEAM1 population were clustered separately, except for the Kashi, Kelamayi, and Urumqi population. The Kashi-5-MED and Urumqi-MEAM1 population were clustered with Kashi-8-MEAM1 and Urumqi-MED. The rest of the Kashi-MED population (Kashi-1, Kashi-2, Kashi-3, Kashi-4, Kashi-6, Kashi-7, and Kashi-13), Kelamayi-MED in northern-Xinjiang, and Hetian-MEAM1 in Southern Xinjiang converged into one branch. Nei’s genetic clustering results showed that there was no significant correlation between genetic relationship and the regional distribution of B. tabaci in Xinjiang.

Fig. 3.

Fig. 3.

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Genetic relationship analyzed by a unrooted neighbor-joining (NJ) phylogenetic tree (A) and principal coordinate analysis (PCoA) plot of Bemisia tabaci complex (B), MED (C), and MEAM1 (D) population in Xinjiang. For NJ trees, green and blue branches indicate population from Southern and Northern Xinjiang, respectively.

Principal Coordinate Analysis

The phylogenetic tree has shown that most of MED and MEAM1 populations could be clearly separated according to geographic clustering. Thus, we verified the relationships between MED and MEAM1 samples with a PCoA plot. Based on 99 alleles of five SSR primers, the top three principle components accounted for 55.9, 67.9, and 70.2% of the SSR variation in MED, MEAM1, MED, and MEAM1 complex. There were no clear boundaries between MED and MEAM1 samples. However, the MED samples from Southern Xinjiang were mainly located at the right side of the coordinate axis, whereas the MEAM1 samples from Northern Xinjiang were mainly located at the bottom (Fig. 2B). We further tested whether MED and MEAM1 samples could be separated by geographic isolation. Results show that MED samples that grouped by sampling sites were highly highly dispersed but can be partially separated into Northern and Southern Xinjiang, with the large Tianshan Mountain potentially serving as a geographical barrier for the two groups (Fig. 2C). MEAM1 samples were clearly separated, especially those from Southern Xinjiang. Interestingly, MEAM1 in Northern Xinjiang occupied the center which separated the samples of Bayingol from those of Hotan and Kashgar.

MEAM1 B. tabaci in northern Xinjiang showed clear separation from those in Hotan, Bazhou, and Kashgar area. MED B. tabaci from northern Xinjiang was intersected by those from Bazhou and Hotan areas. The results showed further that MED B. tabaci individuals and MED B. tabaci from southern Xinjiang were clearly clustered, whereas those from northern Xinjiang were more dispersed. The range of MED from northern Xinjiang overlaps with that of MED B. tabaci from southern Xinjiang.

Gene Flow

The GENECLASS 2.0 assigned individual probabilities to evaluate the degree of migration and the migration direction of MED or MEAM1 groups based on ΔK (Fig. 4). The results indicate that individuals migrated between MED and MEAM1 B. tabaci populations. Most of the highest probabilities were self-assigned in each subdivision or even in local areas. However, probabilities of migration from Urumqi-MEAM1 to Urumqi-MED (0.47), Hetian-11-MEAM1 to Hetian-13-MED (0.44), Changji-MED to Hetian-6-MEAM1 (0.23), Changji-MED to Alashankou-MED (0.32), Changji-MED to Hami-MED (0.28), and Hetian-13-MED to Hetian-11-MEAM1 (0.41) were higher than self-assigned probabilities in their subdivisions.

Fig. 4.

Fig. 4.

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Means of individual assignment probabilities from (rows) and into (columns) each population of Bemisia tabaci in Xinjiang. Populations with immigrating individuals are given in rows; populations with emigrating individuals in columns. Note: Highest assignment probability in rows and means of migration probabilities in each column > 0.20 were red. The green background color deepened as the probability got higher. Subdivision of MED and MEAM1 groups were framed, respectively.

To predict the immigrant origins, we calculated the means of migration probabilities in each column (Fig. 4). Interestingly, we found that populations of Kashi-3-MED (0.22), Kashi-4-MED (0.22), Kashi-5-MED (0.21), Kashi-3-MED (0.20), Hetian-13-MED (0.21), Turpan-5-MED (0.21), Hetian-11-MEAM1 (0.26), and Hetian-11-MEAM1 (0.20) had a relatively high value. Migration assigned probabilities from MED to MED, MED to MEAM1, MEAM1 to MED, and MEAM1 to MEAM1 are also depicted as box diagrams (Fig. 5). Results showed that there was no difference between migration assigned probabilities from MED to MED (mean ± SE = 0.174 ± 0.007) and MEAM1 to MEAM1 (0.206 ± 0.015; F = 0.007, P = 0.977), but there were significant differences between MED to MED and MED to MEAM1 (0.065 ± 0.005; F = 0.184, P < 0.001), MED to MED and MEAM1 to MED (0.119 ± 0.006; F = 0.077, P < 0.001), MEAM1 to MEAM1 and MED to MEAM1 (F = 0.1844, P < 0.001), and finally, MEAM1 to MEAM1 and MEAM1 to MED (F = 0.083, P < 0.001).

Fig. 5.

Fig. 5.

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Box diagram of assigned probabilities of migration from MED to MED, MED to MEAM1, MEAM1 to MED, and MEAM to MEAM1. Probabilities above 95% and below 5% were depicted as dots and 5% and 95% threshold as bars. Horizontal lines and in box indicate quantiles and medians. ‘+’ expresses means. Capital letters mean significant levels at P < 0.001.

To further analyze the gene flow between MED and MEAM1 populations in local regions, Kashi and Hetian populations were selected since they were coexisting populations and there were plenty of sampling points. Kashi-8-MEAM1 was the only MEAM1 population chosen for the SSR analysis out of six randomly selected Kashi populations. Hetian-13-MED was the only population chosen out of 10 randomly selected Hetian populations. MED and MEAM1 showed different migration patterns. For Kashi-8-MEAM1 population, the assigned probability was self-assigned (0.44) and the rest of the Kashi-MED populations were mainly migrants from Kashi-2-MED (0.35–0.67). As for Hetian-13-MED, the gene flows from Hetian-11-MEAM1 (0.44), Hetian-12-MEAM1 (0.38), Hetian-13-MEAM1 (0.36), Hetian-6-MEAM1 (0.34) were higher than the self-assigned (0.28). The gene flow from Hetian-13-MED population to other populations was high (0.39–0.52), except for Hetian-6-MEAM1 population, which received migrants mainly from Changji-MED. Apart from Hetian-6-MEAM1 and Hetian-11-MEAM1, the Hetian-MEAM1 populations received gene flows mainly from Hetian-11-MEAM1 (0.47–0.67) and Hetian-13-MEAM1 (0.69–0.79).

Discussion

MED B. tabaci has spread and gradually replaced the ecological niche of MEAM1 as a dominant cryptic species in many provinces since it was first found in Yunnan in 2004 (Chu et al. 2005). After 22 yr of B. tabaci invasion in Xinjiang, CAPs were used to identify MED and MEAM1 cryptic species in this study, and the randomly chosen SSR samples were also used for sequencing COI gene (data not shown) to verify CAP-based identification. Based on the results of CAPs, we found that MED B. tabaci was mainly distributed in northern Xinjiang and was in agreement with Cao et al. (2013). We also found that in southern Xinjiang, a higher proportion of MEAM1 existed, there was a wide percentage difference between northern (MED: 84%) and southern (MED: 28%) Xinjiang. The investigation revealed that the most serious damage occurred in Turpan area and the MED was the dominant crop pest. This was attributed to local climatic conditions, planting patterns, and field management. The samples from Shihezi, Alashankou, Changji, and Aler were collected in flowers and were identified and confirmed as MED-byrelated studies. The spread of MED B. tabaci is related to the introduction of flowers (Chu et al. 2005). MEAM1 was the main cryptic species in southern Xinjiang including western part of Bazhou, parts of Hotan and Kashgar. This phenomenon could be attributed to the adjacent planting of local vegetable greenhouses, cotton fields, and with frequent flower transport and trade.

We were interested in the invasion pathways of MEAM1 and MED B. tabaci and the effect of substitutions on the internal genetic diversity of B. tabaci. We used five microsatellite primers to analyze the genetic relationship between MED and MEAM1 populations. There was low genetic diversity in B. tabaci in Xinjiang and low genetic variation between populations. The main sources of genetic variation between MED and MEAM1 B. tabaci were interspecific genetic differences and geographical isolation. There was no correlation between the geographical and genetic distances in B. tabaci. The reason is that the man-made transmission factor of B. tabaci is larger than that of the natural transmission. To block ongoing dispersal, strict detection and flower quarantine regulations need to be enforced. Based on the AMOVA and the FST value, B. tabaci populations in Xinjiang had highly significant differentiation. The correlation between the geographical distance (LN Km) and the genetic distance of the whitefly populations was analyzed using Mantel test. Mantel’s t-test demonstrated that there was no relationship between geographical distance and genetic distance (Table 6). ΔK statistic divides MEAM1 into two subgroups and into four subgroups for MED. As the provincial capital, Urumqi may be the main source of MED whitefly transmission, while MEAM1 invasion pathways may be more varied. NJ-tree and PCoA could neither distinguish MED and MEAM1 nor separate the northern and southern Xinjiang into two clusters well (Fig. 3). This may be explained by the gene flow between a few MED and MEAM1 populations, such as Hetian-13-MED, Urumqi-2-MEAM1, and Changji-MED (Fig. 4). The unique characteristics of irrigation agriculture and oasis agriculture in Xinjiang, at the same time, the large desert areas surrounding cities, provide geographical barrier for whitefly transmission. Thus, human-based transmission becomes the main mode of transmission for B. tabaci. MED populations had a stronger gene flow toward MEAM1 populations. The probabilities in MEAM1 were mainly self-assigned, indicating that the MEAM1 had a reproductive disadvantage to MED. This can result from pesticide resistance (Roditakis et al. 2009, Wu et al. 2010) or asymmetric mating interaction (Liu et al. 2007).

Acknowledgments

We thank Sheng Han (Xinjiang Academy of Agricultural Sciences, Xinjiang, China) for the experimental assistance. This work was supported by grants from the Key Cultivation Projects for Scientific and Technological Innovation of Xinjiang Academy of Agricultural Sciences (no. xjkcpy-002); Science and Technology Program under the Xinjiang project, ‘Molecular detection technology study of resistance of Bemisia tabaci’ (no. 201591140); and the National Key Research Project, ‘The dynamic distribution and resource database construction of the main invading organisms’ (no. 2016YFC1202100).

Author contributions

Z.J., K.F., and W.G. conceived and designed the experiments. Z.J., K.F., W.G., T.A., X.D., J.H., X.W. collected samples, performed the experiments and analyzed the data. Z.J., K.F., W.J. and W.G. wrote the paper. All authors read and approved the manuscript.

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