Hostname: page-component-77c89778f8-cnmwb Total loading time: 0 Render date: 2024-07-22T10:29:30.289Z Has data issue: false hasContentIssue false
  • English
  • Français

Genetic differentiation of the pine wilt disease vector Monochamus alternatus (Coleoptera: Cerambycidae) over a mountain range – revealed from microsatellite DNA markers

Published online by Cambridge University Press:  05 April 2007

E. Shoda-Kagaya
E. Shoda-Kagaya*
Affiliation:
Department of Forest Entomology, Forestry and Forest Products Research Institute, Matsunosato 1, Tsukuba, Ibaraki 305-8687, Japan
*
*Fax: +81 29 873 1543 E-mail: eteshoda@ affrc.go.jp
Get access Rights & Permissions [Opens in a new window]

Abstract

To study the dispersal process of the pine sawyer Monochamus alternatus (Hope) in frontier populations, a microsatellite marker-based genetic analysis was performed on expanding populations at the northern limit of its range in Japan. In Asian countries, M. alternatus is the main vector of pine wilt disease, the most serious forest disease in Japan. Sawyers were collected from nine sites near the frontier of the pine wilt disease damage area. A mountain range divides the population into western and eastern sides. Five microsatellite loci were examined and a total of 188 individuals was genotyped from each locus with the number of alleles ranged from two to nine. The mean observed heterozygosity for all loci varied from 0.282 to 0.480 in the nine sites, with an overall mean of 0.364. None of the populations have experienced a significant bottleneck. Significant differentiation was found across the mountain range, but the genetic composition was similar amongst populations of each side. It is believed that the mountain range acts as a geographical barrier to dispersal and that gene flow without a geographical barrier is high. On the west side of the mountain range, a pattern of isolation by distance was detected. This was likely to be caused by secondary contact of different colonizing routes on a small spatial scale. Based on these data, a process linking genetic structure at local (kilometres) and regional spatial scales (hundreds of kilometres) was proposed.

Keywords

dispersalforest pestgenetic structuremicrosatellitepine sawyer
Type
Research Article
Information
Bulletin of Entomological Research , Volume 97 , Issue 2 , April 2007 , pp. 167 - 174
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berry, O., Mandy, M.D., Tocher, D. & Sarre, S.D. (2004) Can assignment tests measure dispersal? Molecular Ecology 13, 551–561.CrossRefGoogle ScholarPubMed
Bohonak, A.J. (1999) Dispersal, gene flow, and population structure. Quarterly Review of Biology 74, 21–45.CrossRefGoogle ScholarPubMed
Cornuet, J.M. & Luikart, G. (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014.CrossRefGoogle ScholarPubMed
De Lamballerie, X., Zandott, C., Vignoli, C., Bollet, C. & de Micco, P. (1992) A one-step microbial DNA extraction method using “Chelex 100” suitable for gene amplification. Research in Microbiology 143, 785–790.CrossRefGoogle ScholarPubMed
Fujioka, H. (1993) A report on the habitat of Monochamus alternatus Hope in Akita Prefecture. Bulletin of Akita Prefecture Forest Technical Center 2, 40–56 (in Japanese with English summary).Google Scholar
Goudet, J. (1995) FSTAT (ver. 1.2): a computer program to calculate F-statistics. Journal of Heredity 86, 485–486.CrossRefGoogle Scholar
Honda, J.Y., Nakashima, Y., Yanase, T., Kawarabata, T. & Hirose, Y. (1998) Use of the internal transcribed spacer (ITS-1) region to infer Orius (Hemiptera: Anthocoridae) species phylogeny. Applied Entomology and Zoology 33, 567–571.CrossRefGoogle Scholar
Hutchison, D.W. & Templeton, A.R. (1999) Correlation of pairwise genetic and geographical distance measures: inferring the relative influences of gene flow and drift on the distribution of genetic diversity. Evolution 53, 1898–1914.CrossRefGoogle Scholar
Kamata, N. (1996) Development of a barrier zone to stop the invasion of pine wilt disease in Japan. pp. 81–90 in Proceedings, International Symposium on Pine Wilt Disease Caused by Pine Wood Nematode. Chinese Society of Forestry, 31 October–5 November 1995, Beijing.Google Scholar
Kawai, M., Shoda-Kagaya, E., Maehara, T., Zhou, Z., Lian, C., Iwata, R., Yamane, A. & Hogetsu, T. (2006) Genetic structure of pine sawyer Monochamus alternatus (Coleoptera: Cerambycidae) populations in Northeast Asia: consequences of the spread of pine wilt disease. Environmental Entomology 35, 569–579.CrossRefGoogle Scholar
Kishi, N. (1995) The pine wood nematode and the Japanese pine sawyer. 302 pp. Tokyo, Thomas Company Ltd.Google Scholar
Lehmann, T., Hawley, W.A., Kamau, L., Fontenille, D., Simard, F. & Collins, F.H. (1996) Genetic differentiation of Anopheles gambiae populations from East and West Africa: comparison of microsatellite and allozyme loci. Heredity 77, 192–208.CrossRefGoogle Scholar
Lessa, E.P. (1990) Multidimensional analysis of geographic genetic structure. Systematic Zoology 39, 242–252.CrossRefGoogle Scholar
Mamiya, Y. & Enda, N. (1972) Transmission of Bursaphelenchus lignicolus (Nematoda: Aphelenchoididae) by Monochamus alternatus (Coleoptera: Cerambycidae). Nematologica 18, 159–162.CrossRefGoogle Scholar
Manel, S., Gaggiotti, O.E. & Waples, R.S. (2005) Assignment methods: matching biological questions with appropriate techniques. Trends in Ecology and Evolution 20, 136–142.CrossRefGoogle ScholarPubMed
Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research 27, 209–220.Google Scholar
Nakamura, K. & Okochi, I. (2002) Longevity and ovarian status of adult Monochamus alternatus Hope fed on non-pine tree species. Journal of the Japanese Forest Society 84, 21–25.Google Scholar
Nei, M. (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590.CrossRefGoogle ScholarPubMed
Nei, M., Maruyama, T. & Chakraborty, R. (1975) The bottleneck effect and genetic variability in populations. Evolution 29, 1–10.CrossRefGoogle ScholarPubMed
Noor, M.A.F., Pascual, M. & Smith, K.R. (2000) Genetic variation in the spread of Drosophila subobscura from a nonequilibrium population. Evolution 54, 696–703.Google ScholarPubMed
Paetkau, D., Calvert, W., Stirling, I. & Strobeck, C. (1995) Microsatellite analysis of population structure in Canadian polar bears. Molecular Ecology 4, 347–354.CrossRefGoogle ScholarPubMed
Piry, S., Alapetite, A., Cornuet, J.-M., Paetkau, D., Baudouin, L. & Estoup, A. (2004) GeneClass2: a software for genetic assignment and first-generation migrant detection. Journal of Heredity 95, 536–539.CrossRefGoogle ScholarPubMed
Rannala, B. & Mountain, J.L. (1997) Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences of the United States of America 94, 9197–9201.CrossRefGoogle ScholarPubMed
Rice, W.R. (1989) Analysing tables of statistical tests. Evolution 43, 223–225.CrossRefGoogle Scholar
Roff, A.I. & Yoccoz, N.G. (2001). Experimental approaches to studying transfer rates in metapopulations. pp. 247–265in Hanski, I.A. & Gilpin, M.E. (Eds) Metapopulation biology. London, Academic Press.Google Scholar
Rousset, F. (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228.CrossRefGoogle ScholarPubMed
Rousset, F. (2001) Genetic approaches to the estimation of dispersal rates. pp. 18–28in Colbert, J., Danchin, E., Dhondt, A.A. & Nichols, J.D. (Eds) Dispersal. Oxford, Oxford University Press.CrossRefGoogle Scholar
Schneider, S., Roessli, D. & Excoffier, L. (2000) Arlequin ver. 2.000: A software for population genetics data analysis. Switzerland, Genetics and Biometry Laboratory, University of Geneva.Google Scholar
Shibata, E., Kawasaki, K. & Takeda, T. (1986) Dispersal movement of the adult Japanese pine sawyer, Monochamus alternatus Hope (Coleoptera: Cerambycidae) in a young pine forest. Applied Entomology and Zoology 21, 184–186.CrossRefGoogle Scholar
SPSS Inc. (1998) SYSTAT var. 9.01 Statistics. Chicago, SPSS Science Marketing Department, SPSS Inc.Google Scholar
Sved, J.A., Yu, H., Dominiak, B. & Gilchrist, A.S. (2003) Inferring modes of colonization for pest species using heterozygosity comparisons and a shared-allele test. Genetics 163, 823–831.CrossRefGoogle Scholar
Togashi, K. (1989) Studies on population dynamics of Monochamus alternatus Hope (Coleoptera: Cerambycidae) and spread of pine wilt disease caused by Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae). Bulletin of Ishikawa-ken Forest Experiment Station 20, 1–145 (in Japanese with English summary).Google Scholar
Turgeon, J. & Bernatchez, L. (2001) Clinal variation at microsatellite loci reveals historical secondary intergradation between glacial races of Coregonus artedi (Teleostei: Coregoninae). Evolution 55, 2274–2286.Google ScholarPubMed
Weir, B.S. & Cockerham, C.C. (1984) Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370.Google ScholarPubMed
Yeh, F.C., Yang, R. & Boyle, T. (1999) POPGENE VERSION 1.31. Microsoft Window-based freeware for population genetic analysis. Quick user guide. 28 pp.Google Scholar