Genetic variation and phylogeographic structure of  Spodoptera exigua  in western China based on mitochondrial DNA and microsatellite markers

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Abstract

The beet armyworm, Spodoptera exigua, is a significant agricultural pest of numerous crops and has caused serious economic losses in China. To effectively control this pest, we analyzed its genetic variation, population genetic structure and demographic history. We used mitochondrial DNA (mtDNA) fragments of the cytochrome oxidase subunit I ( COI) and eight nuclear microsatellite loci to investigate genetic diversity and population genetic structure of S. exigua populations at 14 sampling sites in western China. Both mtDNA and microsatellite data indicated low levels of genetic diversity among all populations. A moderate genetic differentiation among some S. exigua populations was detected. Neighbor-joining dendrograms, STRUCTURE, and principal coordinate analysis (PCoA) revealed two genetically distinct groups: the KEL group and the remaining population group. Isolation by distance (IBD) results showed a weak significant correlation between geographic distance and genetic differentiation. Haplotype networks, neutrality testing, and mismatch distribution analysis indicated that the beet armyworm experienced a recent rapid expansion without a recent genetic bottleneck in western China. Thus, the results of this population genetic study can help with the development of strategies for managing this highly migratory pest.

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Most cited references 47

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Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST<0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.

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Naoko Takezaki, Masatoshi Nei, Koichiro Tamura (2010)

Currently, there is a demand for software to analyze polymorphism data such as microsatellite DNA and single nucleotide polymorphism with easily accessible interface in many fields of research. In this article, we would like to make an announcement of POPTREE2, a computer program package, that can perform evolutionary analyses of allele frequency data. The original version (POPTREE) was a command-line program that runs on the Command Prompt of Windows and Unix. In POPTREE2 genetic distances (measures of the extent of genetic differentiation between populations) for constructing phylogenetic trees, average heterozygosities (H) (a measure of genetic variation within populations) and G(ST) (a measure of genetic differentiation of subdivided populations) are computed through a simple and intuitive Windows interface. It will facilitate statistical analyses of polymorphism data for researchers in many different fields. POPTREE2 is available at http://www.med.kagawa-u.ac.jp/ approximately genomelb/takezaki/poptree2/index.html.

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Author and article information

Contributors

Xing-Ya Wang:

ORCID: http://orcid.org/0000-0001-6850-3809

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draft

Ming-Ming Wang: Role: ConceptualizationRole: Data curationRole: Software

Chen Chen: Role: Formal analysisRole: Software

Xiao-Qi Wang: Role: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing

Rodney N. Nagoshi: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 14 May 2020

Publication date Collection: 2020

Volume: 15

Issue: 5

Electronic Location Identifier: e0233133

Affiliations

[001]College of Plant Protection, Shenyang Agricultural University, Shenyang, Liaoning, China

CMAVE, USDA-ARS, UNITED STATES

Author notes

Competing Interests: NO authors have competing interests.

* E-mail: wangxiaoqi1120@ 123456163.com

Author information

Xing-Ya Wang http://orcid.org/0000-0001-6850-3809

Article

Publisher ID: PONE-D-20-00175

DOI: 10.1371/journal.pone.0233133

PMC ID: 7224464

PubMed ID: 32407374

SO-VID: 1bb7c54e-bb35-4387-926f-d2528ed0b58e

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 3 January 2020

Date accepted : 28 April 2020

Page count

Figures: 5, Tables: 2, Pages: 16

Funding

Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 31871950

Funded by: National Key Research and Development Program of China

Award ID: 2018YFD0200200

Funded by: Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (BR)

Award ID: 2019JH2/10200012

Award Recipient : Xingya Wang

This work was supported by the National Natural Science Foundation of China (Grant No. 31871950); The National Key Research and Development Program of China (2018YFD0200200); The science and technology plan of Liaoning Province (2019JH2/10200012).

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Data Availability All relevant data are within the manuscript and its Supporting Information files.

Genetic variation and phylogeographic structure of Spodoptera exigua in western China based on mitochondrial DNA and microsatellite markers

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