Lower genetic diversity and decreased genetic differentiation in populations of western flower thrips across an invaded range

doi:10.21203/rs.3.rs-1330291/v1

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Lower genetic diversity and decreased genetic differentiation in populations of western flower thrips across an invaded range

https://doi.org/10.21203/rs.3.rs-1330291/v1

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published 13 Feb, 2023

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Colonizing populations of alien invasive species are often unstable due to their small population size, ongoing gene flow, and ongoing adaptation to local conditions. These processes should lead to molecular signatures at the population level. However, temporal changes in genetic patterns after introduction are rarely examined. The western flower thrips (WFT), Frankliniella occidentalis (Pergande), is a globally invasive pest of vegetables and ornamental crops. Early colonized populations in China exhibited a strong population structure reflecting different invasion sources. Here, we re-examine this pattern after ten years. Over this period, the number of alleles has declined significantly and strong population genetic structure found early in the invasion stage has been lost. A high level of gene flow between both geographically close and distant populations was identified. The reduced allele number and loss of population structure might reflect both ongoing gene flow and ongoing control measures to suppress populations. Our results emphasize the importance of continuing to manage gene flow among invaded areas to limit the exchange of alleles that might facilitate further adaptive changes following invasion.

Frankliniella occidentalis

alien invasive species

population genetics

genetic admixture

temporal variation

Alien invasive species are having a major impact on biodiversity, ecosystem functioning, agriculture, and public health (Diagne et al. 2021). Increased international trade and transportation is leading to an increased rate of biological invasions (Seebens et al. 2017). Once an invader has established in a new area, evolutionary processes can affect its further spread and impact on its management (Bock et al. 2015; Estoup et al. 2016; Lee 2002; Ortego et al. 2021).

An invading population often starts out being small compared with populations of related resident species (Estoup et al. 2016; Schmack et al. 2019), increasing the likelihood of genetic drift and loss of alleles (Semenov et al. 2019). In order to eradicate or prevent the spreading of invading populations, intensive control efforts are usually conducted, which further decrease the size of newly colonized populations (Branco et al. 2022) and increase the potential for genetic drift. The resulting loss of genetic diversity may in turn limit the adaptive ability of the newly founded populations because of the possible loss of selectively favoured alleles (Reed, Frankham 2003; Reusch et al. 2005). However, many invading populations successfully colonize new areas despite low genetic variation (Facon et al. 2006; Tsutsui et al. 2000).

Several studies have examined evolutionary processes before and after an invasion, providing insights into the invasion genetics of species (Bock et al. 2015; Estoup et al. 2016; Medley et al. 2019; Sherpa, Després 2021). However, few studies have tracked temporal variation in genetic diversity following invasions. While some species have maintained population genetic diversity during invasion (Brock et al. 2021), others have lost genetic variation (Cao et al. 2016b; Schmack et al. 2019; Xue et al. 2018) or it has been further bolstered by new incursions (Quaresma et al. 2022).

Where low genetic diversity of invading populations in the early stages of introduction is bolstered by ongoing gene flow (Smith et al. 2020; van Boheemen et al. 2017), admixture from different sources may accelerate the rate of invasion by providing rapid adaptive evolution (Qiao et al. 2019; Rius, Darling 2014; Smith et al. 2020). When the colonized environment is different from the native environment, the adaptive benefits of admixture are expected to be high (Verhoeven et al. 2011). Climate change is also expected to pose strong selection on invading populations favouring novel combinations of genes (Chown et al. 2015; Ma, Ma 2022; Ravi et al. 2022). Tracking genetic changes in invasive populations can therefore be useful in understanding likely current and future trajectories of these populations.

The western flower thrips (WFT), Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), is a globally important invasive pest in agriculture and horticulture. The WFT is native to western North America, mainly west of the Rocky Mountains, from Mexico as far north as Alaska. It has spread rapidly worldwide mainly through the movement of horticultural material since the late 1970s (Kirk, Terry 2003). In China, the WFT was first discovered at an international flower show in Kunming, Yunnan Province, in 2000. The first report of the WFT establishment was on greenhouse peppers in the suburbs of Beijing in 2003. WFT rapidly colonized all suitable areas of China in about ten years after the first report and became a common pest on vegetables and flowers. As a small insect, WFT is easily dispersed over long distances by human activities (Cao et al. 2017).

Previous genetic studies suggested bottlenecks and genetic differentiation among populations of WFT in China in the early stages of establishment (Cao et al. 2017; Yang et al. 2015; Yang et al. 2012). Cao et al. (2017) in particular noted strong population structure in populations from across Beijing and suggested multiple introductions of WFT into this region. Here we reconsider these patterns in more recent collections of WFT, including some samples obtained from the same location.

We hypothesized that changes in population genetic structure might occur because of the rapid dispersal of the WFT and therefore potentially high levels of gene flow among populations. However, if new genetic introductions have occurred into China, we expected new patterns of genetic structure to have emerges. We had no clear predictions about levels of genetic variation in the population given that these might be decreased by control measures decreasing population size, whereas gene flow was expected to increase genetic variation through the introduction of new alleles. We also consider insights from our results for invasion genetics generally and for the management of this pest specifcally.

Sample collection and DNA extraction

We collected adult WFT from 12 locations across its distribution in China from 2018 to 2021, ten years after its establishment (Table 1, Figs. 1a and 1b). Within each locality, we sampled WFT from different plants at least five meters apart and kept specimens from the same plant in a tube. The sampled WFT were first identified morphologically using a microscope. All samples were stored in 98% alcohol before DNA extraction. We chose 288 females from different host plants for genotyping. The sampled populations were close to many of the populations tested earlier (Cao et al. 2017) which had been collected from 2005 to 2010 and were considered early colonized populations. A native population from USA was also sampled in the earlier work (Table 1, Fig. 1).

Table 1

Sample information for *Frankliniella occidentalis* used in the study
Group	Population	Location of collecting site	Longitude (°E)	Latitude (°N)	Collecting year	Host plant
Samples collected in late invasion stage in this study	LNMHS	Region Hohhot, Inner Mongolia Autonomous	111.7993	40.7194	2020	Pepper
	LBJYL	Lvfulong, Yanqing District, Beijing	116.0749	40.5520	2020	Cucumber
	LBJYZ	Zhuojia Village, Yanqing District, Beijing	115.9948	40.5141	2021	Strawberry
	LBJCP	Xiaotangshan, Changping District, Beijing	116.4562	40.1795	2021	Eggplant
	LBJHD	Haiding District, Beijing	116.2929	39.9497	2021	Lettuce
	LBJPG	Pinggu District, Beijing	117.0018	40.1066	2020	Towel gourd
	LBJTZ	Tongzhou District, Beijing	116.5672	39.7553	2021	Pepper
	LBJDJ	Qingyundian Town, Daxing District, Beijing	116.5543	39.6740	2020	Pepper
	LBJDQ	Yufa Town, Daxing District, Beijing	116.3221	39.5168	2020	Pepper
	LBJFS	Fangshan District, Beijing	115.9604	39.6079	2021	Towel gourd
	LYNDL	Midu County, Dali Prefecture, Yunnan Province	100.5874	25.2094	2018	Pea
	LYNYX	Yuxi City, Yunnan Province	102.5536	24.3577	2020	Pea
Samples collected in early invasion stage in Cao et al. 2017	EYNHH	Honghe Prefecture, Yunnan Province,	102.4206	23.3692	2010	Corn
	EGZGY	Guiyang, Guizhou Province	106.6302	26.6477	2010	Chinese rose
	ELNCY	Chaoyang, Liaoning Province	120.4504	41.5737	2009	Hollyhock
	EXJWL	Urumqi, Xinjiang Province	87.2313	43.3899	2009	Cucumber
	EXZLS	Lhasa, Tibet Province	91.1409	29.6456	2010	Cucumber
	EBJMT	Mentougou, Beijing	116.1017	39.9403	2010	Pepper
	EBJYQ	Lvfulong, Yanqing District, Beijing	115.9750	40.4567	2010	Cucumber
	EBJFS	Hancunhe, Fangshan District, Beijing	115.9643	39.6020	2006	Cucumber
	EBJHD	Haidian District, Beijing	116.2872	39.9439	2006	Spinach
	EUSCA	San Diego city, California Prefecture, USA	-117.1573	32.7153	2005	Pea
The first letter of the population code represents the period of collection (L, late invasion stage; E, early invasion stage).

Total genomic DNA was extracted by homogenizing each WFT sample in an 80 µl mixture of STE buffer (50 mM KCl, 10 mM Tris-HCl, 2.5 mM MgCl₂, 0.45% Tween-20, 0.01% Gelatin) and 0.8 µl proteinase K (20 mg/ml) in 96-well plates using a high-throughput tissue grinder, SpexSample prep 1600 MiniG (SPEX SamplePrep, LLC, Metuchen NJ, USA). The mixture was incubated at 37°C for 30 minutes and then at 95°C for 5 minutes. The samples were centrifuged briefly and used immediately for PCR amplification or stored at -20°C.

Microsatellite genotyping

Due to the small body size and low quantity of extracted genomic DNA from each individual, it was not possible to directly retest material from Cao et al. (2016a). However, to ensure the same markers were used, we characterized the new samples for variation at the 16 microsatellite loci used in Cao et al. (2016a). The PCR amplification reaction was carried out in a 10 µL volume consisting of 1 µL of template DNA (1–3 ng/µL), 5 µL of Master Mix (Promega, Madison, WI, USA), 0.08 µL of PC tail modified forward primer (10 mM), 0.16 µL of reverse primer (10 mM), 0.32 µL of fluorescence-labeled PC tail (10 mM), and 3.44 µL of ddH₂O. The thermal profiles for DNA amplification were as follows: 4 min at 94°C; 35 cycles of 30 s at 94°C, 30 s at 56°C, and 45 s at 72°C, followed by a final 10 min extension at 72°C. The size of amplified PCR products was determined using an ABI 3730xl DNA Analyzer with GeneScan 500 LIZ size standard. The microsatellite loci were genotyped using GENEMAPPER version 4.0 (Applied Biosystems, USA) by incorporating the panels of Cao et al. (2016a).

Genetic diversity analysis

GENEPOP version 4.2.1 (Rousset 2008) was used to test for linkage disequilibrium between pairs of loci in each population and deviations from Hardy-Weinberg equilibrium (HWE) at each locus/population combination based on Fisher’s exact tests. The population, genetic diversity indices, including the total number of alleles (A_T), and the unbiased expected heterozygosity (H_E) (Nei 1978) were assessed with GENCLONE version 2.0 (Arnaud-Haond, Belkhir 2007). We compared the number of alleles (A_S) and heterozygosity (H_ES) among samples with different sample sizes using a rarefaction method in GENCLONE. Allelic richness (A_R) and allelic richness of private alleles (P_AR) were calculated with a rarefaction approach in HP-RARE version 1.1 (Kalinowski 2005) on a minimum sample size of 22 diploid individuals. In addition, the significance of differences in the diversity parameters of the WFT between the early colonized and contemporary populations was analyzed using a rank-sum test in the IBM SPSS Statistics version 26.

Population genetic structure analysis

The Bayesian model-based clustering method implemented in STRUCTURE version 2.3.4 (Falush et al. 2007), and discriminant analyses of principal components DAPC were used to infer the genetic structure of WFT. In the STRUCTURE analysis, an admixture model with correlated allele frequencies was used. Thirty replicates for each K (from 1 to 10) were run with 200,000 Markov chain Monte Carlo (MCMC) iterations after a burn-in of 100,000 iterations. The STRUCTURE outputs were submitted to STRUCTURE HARVESTER WEB version 0.6.94 (Earl, Vonholdt 2012) to estimate the optimal value of K with the Delta (K) method (Evanno et al. 2005). The membership coefficient matrices (Q-matrices) of replicated runs for each K were combined using CLUMPP version 1.1.2 (Jakobsson, Rosenberg 2007) with the Greedy algorithm and then visualized with DISTRUCT version 1.1 (Rosenberg 2004). DAPC was performed using the package adegenet version 2.0.1 (Jombart, 2008) in the R environment to identify the number of different genetic clusters.

Gene flow analysis

Based on microsatellite data, recent migration rates among populations were estimated with BAYESASS 3.0.4 (Wilson & Rannala, 2003). We performed preliminary runs (10,000,000 steps) to adjust mixing parameters for allele frequencies and inbreeding coefficients. We then conducted ten longer runs of 100,000,000 steps using different start seeds and a sampling frequency of every 1,000 steps. Trace files of the ten longer runs were combined to calculate mean migration with a burn-in of 10,000,000 with TRACER 1.6.

Temporal variation of population genetic diversity

All invaded populations collected from China had lower genetic diversity than the native population from USA (Table 2). All average genetic diversity indices increased in the populations from China sampled later except for private allelic richness, which decreased. There weres significant difference in the average values of allelic richness (A_R: SE = 15.157; Z = 3.167; p = 0.001), total number of alleles (A_T: SE = 15.140; Z = 2.279; p = 0.021) and standardized total number of alleles (A_s: SE = 15.166; Z = 2.506; p = 0.011) between the early and late population groups (Table 2). Compared to populations collected at the early invasion stage, we found 77 alleles missing and 15 new alleles in populations collected more recently.

Table 2

Genetic diversity of *Frankliniella occidentalis* populations and statistical difference between populations collected in two invasive stages
Group	Population	N	A_R	P_AR	A_T	A_S	H_O	H_ET	H_ES	F_IS
Late invasion stage	LYNYX	24	6.64	0.17	128	150.00	0.64	0.704	0.731	0.093
	LYNDL	24	6.36	0.15	121	130.00	0.71	0.727	0.732	0.030
	LBJDQ	24	6.90	0.15	131	129.00	0.68	0.734	0.733	0.068
	LBJFS	24	6.50	0.06	122	143.00	0.62	0.731	0.760	0.149
	LBJTZ	24	6.11	0.07	114	152.00	0.65	0.692	0.770	0.067
	LBJDJ	24	6.69	0.20	126	172.00	0.63	0.727	0.813	0.137
	LBJHD	24	6.51	0.12	123	160.00	0.57	0.744	0.796	0.236
	LBJPG	24	6.17	0.16	117	135.00	0.68	0.731	0.757	0.073
	LBJCP	24	6.62	0.04	125	123.00	0.65	0.725	0.722	0.103
	LBJYZ	24	6.41	0.14	120	142.00	0.70	0.716	0.740	0.024
	LBJYL	24	5.94	0.02	110	165.00	0.59	0.689	0.785	0.152
	LNMHS	24	6.36	0.20	123	125.00	0.65	0.700	0.709	0.069
Early invasion stage	EBJFS	39	3.37	0	64	65.55	0.48	0.540	0.564	0.109
	EBJHD	29	4.36	0.15	86	83.06	0.58	0.624	0.644	0.080
	EBJMT	30	4.51	0.12	91	95.82	0.58	0.612	0.644	0.051
	EXZLS	17	5.38	0.22	96	137.00	0.61	0.652	0.767	0.074
	EBJYQ	22	5.66	0.22	106	123.38	0.61	0.700	0.760	0.135
	EXJWL	27	5.66	0.19	114	120.51	0.66	0.698	0.727	0.059
	EGZGY	39	5.71	0.10	121	114.94	0.68	0.712	0.723	0.039
	EYNHH	30	5.79	0.33	121	131.45	0.62	0.694	0.731	0.112
	ELNCY	29	5.93	0.20	118	120.58	0.63	0.734	0.756	0.144
	EUSCA (native USA)	19	10.09	3.36	198	233.41	0.74	0.866	0.900	0.148
Statistical comparison (early – late)	Standard error (SE)		15.157	15.123	15.140	15.166	14.998	15.166	15.166
	Standardized test statistics (Z)		3.167	-1.620	2.279	2.506	1.000	1.714	1.253
	Precise significance (P)		0.001	0.107	0.021	0.011	0.346	0.093	0.228
Sample size (N), A_R, average allelic richness (for 22 specimens); P_AR, private allelic richness (for 22 specimens); A_T, total number of alleles; A_S, standardized total number of alleles for 16 specimens per samples. H_ET, expected heterozygosity; H_ES, standardized expected heterozygosity (for 16 specimens); F_IS, inbreeding coefficient. See Table 2 for abbreviations of genetic diversity parameters.

Table 3

Number of missing and new alleles of 16 microsatellite loci of *Frankliniella occidentalis* between populations of two collection periods
Type	S08	S43	S58	S09	S17	S36	S14	S29	S50	S61	S66	S85	S93	S69	S89	S96	Total
Missing allele	1	5	5	3	10	2	3	3	4	6	6	9	4	4	3	9	77
New allele	0	0	1	0	2	1	2	1	1	1	0	1	1	2	2	0	15
The 16 microsatellite loci of Frankliniella occidentalis, the number of alleles lost or gained during the two collection periods, with losses ranging from 1 to 10, and new alleles ranging from 0 to 2, producing totals of 77 and 15 respectively.

Temporal variation of population genetic structure

We analyzed the population genetic structure across all populations, the early colonized populations and the more recently sampled populations (Fig. 1). When all populations were considered, they could be optimally divided into three genetic clusters. The first cluster (blue in Fig. 1a when K = 3) comprised of the southwestern, northeastern, and northwestern regions. The second cluster (pink in Fig. 1a when K = 3) comprised of populations collected from the Beijing area and one northeastern population (ENMHS). The third cluster (orange in Fig. 1a when K = 3) involved three Beijing populations collected in the early invasion stage. The latter three populations showed the highest pairwise F_ST values in comparison to the other populations (Table 4). When we decreased the K value to 2, the first two clusters collapsed into one, but the third cluster remained. This third cluster was not detected in populations sampled recently. Similar results were obtained when populations from the two sampling periods were analyzed separately. These analyses showed a strong population genetic structure at the early invasion stage (Fig. 1d) and a lack of population structure and a high level of admixture among populations in the most recent collections, although two Yunnan populations (LYNYX and LYNDL) were different from the other populations in that they were composed mostly of one origin (Fig. 1e).

The DAPC revealed similar patterns to those obtained from the STRUCTURE clustering analyses. Three populations collected from Beijing at the early invasion stage were separated from other populations (Fig. 3a). When these three outlier populations were removed from the analysis, two Yunnan populations (LYNYX and LYNDL) were somewhat separated from others which all overlapped (Fig. 3b).

Gene flow

The estimates of gene flow obtained from the BAYESASS analysis were consistent among ten independent runs, and the mean migration rates of each population are provided as a heat map (Fig. 3). Among populations collected at the early invasion stage, a higher rate of gene flow was found across long geographical distances (i.e., EGZGY and EBJYQ) and between close populations (i.e., EBJFS and EBJHD, EBJFS and EBJMT) (Fig. 3a). Gene flow between long-distance and close populations was also detected in populations collected more recently. These results point to ongoing gene flow among populations of WFT resulting in a lack of population structure in recent collections.

In this study, we compared genetic diversity and population structure between populations of the invading WFT collected at the early invasion stage in China and more recently. We found a decline of genetic diversity after ten years in the colonized populations of WFT. We also found that a unique genetic cluster previously identified in Beijing area ten years ago had been lost. Here we discuss processes likely to have contributed to the loss of some alleles and loss of population structure.

The loss of allelic variation may reflect changes in population size of WFT. Because this pest causes severe damage on agriculture and horticulture, pesticides are frequently applied to control WFT (Mouden et al. 2017; Reitz et al. 2020). In practice, a limited number of pesticides are effective against these thrips except for the spinosyns (Reitz et al. 2020). At the early invasion stage, the WFT populations are likely to have been mostly susceptible to the spinosyns unlike the co-occurring thrips Thrips palmi (Gao et al. 2019; Wang et al. 2016). This susceptibility likely accounted for a decline in the abundance of WFT relative to T. palmi (Gao et al. 2019). As WFT were initially controlled using pesticides, sharp decreases in population size may have led to allele loss in the invading populations. It has previously been noted that population diversity of the invasive fall webworm, Hyphantria cunea, in China was probably also affected by control efforts (Cao et al. 2016b). However, due to high fecundity, high resistance to many other insecticides, and high population growth potential (Reitz et al. 2020), WFT are expected to recover rapidly from control efforts despite this loss. Moreover, the haplodiploid sex determination of WFT makes them relatively resistant to the detrimental effects of inbreeding.

Invasive species may show genetic structure in their introduced regions as a result of multiple introductions (Konecny et al. 2013), dispersal patterns following invasion (Berthouly-Salazar et al. 2013) and genetic drift following control efforts (Cao et al. 2016b). However, this can be followed by admixture which is particularly prevalent in species with low dispersal capabilities and widespread ranges (Goldstien et al. 2011; Gunn et al. 2011; Lombaert et al. 2010; Pineda et al. 2011; Zhan et al. 2012), a paradox that has typically been explained as a result of anthropogenic transport. Here we note admixture in recent collections that include two formerly differentiated populations from Beijing collected at the same location across the two sampling points (EBJFS vs LBJFS and EBJHD vs LBJHD). We suspect that movement of flowers and seedlings between different regions would have enhanced gene flow between WFT populations. WFT are only weak active flyers that deposit eggs inside plant tissue and with a polyphagous nature (feeding on over 250 different plants in 62 different families) make detection difficult (Brunner, Frey 2010; Yang et al. 2012). WFT may also be wind-dispersed but do not actively select preferred habitats over long distances. Instead, passive movement of WFT is likely across China in multiple directions. Yunnan Province is a major producer of fresh-cut flowers and vegetables and is known as the "hometown of tea and flowers." The products are shipped to other regions of China and other countries. Beijing is an international metropolis and mainly depends on importing agricultural and horticultural products from other places and local products. However, the Yanqing District of Beijing is an important vegetable production area with goods transported to other areas of Beijing. Transportation of vegetables and flowers would be expected to have made WFT populations more genetically homogeneous across time.

This study reveals the new knowledge of WFT, which can help improve management strategies. Due to the high gene flow across WFT populations, it is difficult to control this pest locally without focusing on human-assisted movement. Preventing the movement of contaminated plants from putative source areas needs to be combined with population suppression or local eradication efforts. In the long run, genetic admixture of divergent sources may enhance the adaptive potential of introduced populations and facilitate further evolutionary changes that increase the areas colonized by this pest. A growing number of studies demonstrate a linkage between admixture and phenotypic outcomes with potential relevance to colonization success. Such correlations have been observed in non-indigenous plants (Chun et al. 2011; Keller, Taylor 2010; Qiao et al. 2019; Song et al. 2021), invertebrates (Turgeon et al. 2011), and vertebrates (Aguilar-Velazquez et al. 2021; Kolbe et al. 2007; Kolbe et al. 2008; Nolte et al. 2005). It is therefore worthwhile to consider the long-term impact of admixture of WFT while also evaluating the risk of further introductions that might introduce new genetic variants into WFT populations in China.

In conclusion, we found that the WFT population introduced in China have become admixed across time, a pattern that is particularly evident in the Beijing area. While some alleles appear to have been lost from populations, there is sufficient gene flow across WFT populations in China that could facilitate the spread of new alleles across populations, perhaps facilitating evolutionary changes that might raise further challenges in controlling this pest.

Acknowledgement

We thank Bin-Shuang Pang and Ming-Ming Zhang for their assistance with molecular works.

Funding

This research was financially supported by Beijing Natural Science Foundation (JQ21021), Promotion and Innovation Program of Beijing Academy of Agricultural and Forestry Sciences (KJCX20220409), Joint Laboratory of Pest Control Research Between China and Australia (Beijing Municipal Science & Technology Commission, Z201100008320013). AAH is supported by the AGPIP initiative of the Grains Research and Development Corporation.

Conflict of interest

The authors have no conflict of interest to declare that are relevant to the content of this article.

Data availability statement

The datasets generated during and/or analysed during the current study will be available in the Dryad repository.

Author Contributions

Shu-Jun Wei contributed to the study conception and design. Li-Na Sun, Li-Jun Cao, Jin-Cui Chen, Li-Jun Ma and Gui-Fen Zhang prepared the materials and analyzed data. Li-Na Sun and Shu-Jun Wei wrote the first draft of the manuscript. Ary Anthony Hoffmann, Shu-Jun Wei and San-An Wu critically revised the manuscript.

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Lower genetic diversity and decreased genetic differentiation in populations of western flower thrips across an invaded range

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Abstract

Figures

Introduction

Materials And Methods

Sample collection and DNA extraction

Microsatellite genotyping

Genetic diversity analysis

Population genetic structure analysis

Gene flow analysis

Results

Temporal variation of population genetic diversity

Temporal variation of population genetic structure

Gene flow

Discussion

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1