- Research article
- Open Access
Genetic structure of the oak wilt vector beetle Platypus quercivorus: inferences toward the process of damaged area expansion
© Shoda-Kagaya et al; licensee BioMed Central Ltd. 2010
- Received: 22 January 2010
- Accepted: 15 October 2010
- Published: 15 October 2010
The ambrosia beetle, Platypus quercivorus, is the vector of oak wilt, one of the most serious forest diseases in Japan. Population genetics approaches have made great progress toward studying the population dynamics of pests, especially for estimating dispersal. Knowledge of the genetic structuring of the beetle populations should reveal their population history. Using five highly polymorphic microsatellite loci, 605 individuals from 14 sampling sites were assessed to infer the ongoing gene flow among populations as well as the processes of expansion of damaged areas.
Population differentiation (FST = 0.047, G'ST = 0.167) was moderate and two major clusters were detected by several methods, dividing the samples into north-eastern and south-western populations, a similar genetic divergence was reported in host oak trees. Within the north-eastern populations, the subgroups mostly corresponded to differences in the collection period. The genetic characteristics of the population might have changed after 2 years due to the mixing of individuals between populations with enhanced migration related to population outbreaks. Because isolation by distance was detected for whole populations and also within the north-eastern populations, migration was considered to be limited between neighbouring populations, and most populations were suggested to be in genetic equilibrium of genetic drift and gene flow. Recent bottlenecks were found in some populations with no geographical bias; however, they were all from newly emerged oak wilt forests. The emergence of oak wilt should have induced intense fluctuations in the beetle population size.
Because the genetic boundaries coincide, we suggest that the geographical structuring of the beetle was formed by co-evolution with the host species. Our findings indicate the oak wilt expansion process.
- Beetle Population
- Ambrosia Beetle
- Population Outbreak
- Ongoing Gene Flow
- Sequential Bonferroni Procedure
Understanding the processes that drive the spread of plant diseases by vector beetles is critical for developing plans to protect against such damage. Population genetic approaches have made great progress toward studying the population dynamics of pests, especially for estimating dispersal (e.g., [1, 2]). Here we report the population genetic structure of an oak forest pest, Platypus quercivorus (Murayama; Coleoptera: Platypodidae), and infer the ongoing gene flow among populations.
The range of the damaged area has been expanding, but where the beetles originated remains unknown. Knowledge of the genetic structuring of the beetle populations should reveal their population history. Such information may also contribute to an understanding of why the oak wilt has been so damaging. Kamata et al.  suggested that global climate change has pushed the beetle's distribution northward such that it is now attacking Q. crispula, which is a typical species of northern cool-temperate forests in Japan. In addition, oak wilt is less damaging to Quercus serrata Thunb. ex Murray, which is more common in southern areas, than to Q. crispula. Kamata et al.  questioned whether fungus, vector and host species are in evolutionary equilibrium to test the invasive species hypothesis. It is possible that Q. crispula is more susceptible to the disease because of a lack of history of co-evolution between the host and the vector species. Kobayashi and Ueda  predicted that changes in the management of oak forests are the main cause of the increase in oak wilt because P. quercivorus was widely distributed in Japan before the epidemics. Specifically, they suggested that the beetles use mature trees, whose prevalence has increased in oak forests following the disuse of firewood. Changes in the distribution of the beetles may be traced using population genetics methods, and the cause of the sudden disease expansion may be inferred using molecular ecological studies.
To develop plans for protection against oak wilt, it is necessary to understand the process by which the vector beetles spread the disease. Temporal and spatial patterns of P. quercivorus infestations have suggested that the damage spreads from an epicentre to surrounding trees . The majority of disease dispersal is found within the small migration range of the vector; however, damaged areas more than 10 km apart have suddenly emerged . For example, in 2005, Nagano Prefecture reported that after gradual diffusion from the northern region of the prefecture, damaged Q. serrata was found 100 km south of the original infestation . It is unclear whether the expansion of the disease is accomplished by colonisation of the vector beetles. There is increasing interest in adopting new technology to estimate dispersal because the movement of organisms from one location to another is integral to the study of evolutionary and ecological dynamics. Genetic markers are valuable tools for analysing the dispersal abilities and mechanisms, and provide an alternative to mark-and-recapture methods (e.g., ). Areas of ongoing gene flow are often deduced to describe and interpret patterns of genetic structure. Microsatellite markers in particular exhibit high resolution and are suitable for studying genetic structuring and deducing the ongoing dispersal of pests [17, 18]. We used microsatellite markers to map the spatial and temporal population structure of P. quercivorus in areas damaged by oak wilt disease. We investigated the dispersal of P. quercivorus among populations to thereby gain an understanding of the expansion mechanisms of the disease.
Sample sites and sample size of Platypus quercivoru s
Sampling site and abbreviation
Year of the damage emergence
1) Nikaho, Akita Pref. NKH
Jul 9 to Aug 27, 2007
2) Yuza, Yamagata Pref. YUZ
Jun 30 to July 13, 2005
3) Sakata, Yamagata Pref. SAK
Jul 13, 2005
4) Tsuruoka, Yamagata Pref. TOK
Jun 30, 2005
5) Budou, Asahi, Niigata Pref. BDU
Jun 30 to Jul 7, 2005
6) Tamagawa, Oguni, Yamagata Pref. TMG
Jul 10 to Aug 30, 2007
7) Ohmiya, Oguni, Yamagata Pref. OMY
Jul 26 to Aug 30, 2007
8) Sakae, Nagano Pref. SKE
Jul 11 to 25, 2007
Jul 11 to 17, 2007
9) Shinano, Nagano Pref. SNN
Jun 30 to July 4, 2005
10) Tenryu, Nagano Pref. TRL
Nov 2, 2005
11) Tenryu, Nagano Pref. TRT
Jul 19 2007
12) Nagoya, Aichi Pref. NGY
Oct 25, 2007
13) Ogadani, Kyoto, Kyoto Pref. ODN
Jul 6 to Aug 10, 2005
14) Memedani, Kyoto, Kyoto Pref. MMD
Jul 6 to Aug 10, 2005
DNA extraction and microsatellite genotyping
In total, 20-154 individuals were examined per site (Table 1). Total DNA was extracted using PrepMan reagent (Applied Biosystems) or the Chelex method  with some modifications . Five microsatellite loci were examined: Pq3003, Pq3313, Pq3422, Pq3423 and Pq3469 . One primer of each primer set (typically the forward primer) was end-labelled with NED (Pq3422, Pq3423), FAM (Pq3003, Pq3469) or HEX (Pq3313) fluorescent dye. The polymerase chain reaction (PCR) was performed in 8- μl volumes with one-fifth volume of the 5 × PCR buffer Ampdirect containing Mg2+ (Shimadzu Biotech) or one-tenth volume of the 10 × PCR buffer supplied with Ex Taq, 160 μM of each dNTP, 0.4 unit of TaKaRa Ex Taq (TaKaRa), 40 ng of fluorescently labelled primer, 40 ng of the corresponding primer (Applied Biosystems) and genomic DNA. Because the primer pairs Pq3469, Pq3423 and Pq3313 are suitable for multiplex PCR, they were added to one tube with 1/3 concentrates of the mixture described above. Amplification was performed as follows: 94°C for 2 min, followed by 30 cycles of 94°C for 30 s, 53°C for 30 s and 72°C for 45 s. For multiplex loading, 0.5 μl of each PCR product and 0.5 μl of GeneScan 400HD [ROX] Standard (Applied Biosystems) were added to 12 μl of deionised formamide. The products were separated using capillary electrophoresis (ABI PRISM 310; Applied Biosystems) and assigned scores using 310 GeneScan software (Applied Biosystems) and by eye.
Micro-Checker v. 2.2.3 [23, 24] was used to check microsatellite data for scoring errors and null alleles. Fundamental genetic parameters were calculated over all loci using the programme POPGENE v. 1.31 . Observed (HO) and expected heterozygosities (HE) were calculated to quantify the genetic diversity of each population. Allelic richness (A; ) was estimated using a fixed sample size of 20. Putative linkage disequilibrium was assessed between all pairs of loci for each population using GENEPOP , and the significance of the P value was corrected using the sequential Bonferroni procedure . F statistics were studied with FSTAT v. 2.9.3 , estimated FIS and FST to test for local inbreeding within populations and differentiation between populations. The significance of deviations from Hardy-Weinberg equilibrium within population was tested by randomisation. The significance of the population differentiation was tested assuming Hardy-Weinberg equilibrium within populations by the permutation test. The genetic diversity and genetic components were compared between trap-collected and trunk-collected samples in Sakae using the indices above. The post hoc test was adjusted using the sequential Bonferroni procedure . Since the absolute values of the population differentiation parameter FST depend on the level of genetic variations in the examined material, standardised values of G'ST, which always range from 0 to 1, were also calculated using averaged values of intra-population gene diversity (HS), total gene diversity (HT) and the GST values calculated by SMOGD . BOTTLENECK v. 1. 2. 02  was used to detect recent bottlenecks under the assumptions of the infinite allele mutation model (IAM), and the Wilcoxon signed rank test was applied. Severe reductions in population size were inferred from comparisons between the expected equilibrium gene diversity (HE) and the observed numbers of alleles.
An analysis of molecular variance (AMOVA) was performed using the programme ARLEQUIN v. 3.01  to test for a geographical structure effect. An AMOVA was also used to partition the genetic variance among groups, among populations within groups and within populations. Groups were defined as individuals collected in the same year. Upon conducting the AMOVA, FST was used to quantify the degree of population differentiation. The significance of any differentiation was tested using a permutation method with 10,000 replications. Multidimensional Scaling (MDS) based on Nei's genetic distances DA calculated using Populations 1.2.30  was used to explore the relationship between geographical site and genetic differentiation. MDS was performed using the programme SYSTAT v. 9.01 . Multidimensional scaling operates directly on dissimilarities; therefore, no assumptions about statistical distribution were necessary . This avoided the distortions seen in classical scaling methods, which assume a linear relationship between values (e.g., principal co-ordinates analysis PCoA) . The fit of the data in two dimensions was measured by the stress factor. A spatial analysis of molecular variance (SAMOVA, ) algorithm was used to define the population configuration. Given the number of groups (K), the population configuration with the highest differentiation among groups (ΦCT) was calculated using a simulated annealing procedure by SAMOVA 1.0 . K was set between 2 and 9 with 500 independent simulated annealing processes, and the optimum number of population groups for a set of sample populations was estimated by exploring the behaviour of the index ΦCT for different values of K. Clustering of the populations was assessed by generating a neighbour-joining (NJ) tree based on the DA genetic distance using Populations with a bootstrapping test.
"Isolation by distance" (IBD) was tested as the correlation between genetic and geographical distance, using all population pairs to estimate the regression of FST/(1 - FST) on a logarithm of distance for populations, as suggested by Rousset . The relationship between genetic differentiation and geographic distance was assessed by the Mantel test , with 9,999 randomisations using GenAlEx 6.2 . This test was done for total populations as well as populations collected in same year (2005 and 2007). The reasoning for the latter analysis is discussed in the Results.
To assess the level of population structure and assignment of an individual's origin, an individual-based clustering was performed using the programme STRUCTURE v. 2.3.1 . F statistics and AMOVA tests used the sample location as the unit of comparison, whereas the Bayesian model-based methods of STRUCTURE used the individual as the unit, assigning it to the most likely group (cluster). Each individual genotype was used to estimate the proportion of admixture from several demes in an individual's nuclear genome. Five independent runs for each K were used, with burn-ins of 20,000 replicates and run lengths of 10,000 replicates. An allele frequency correlated model (F model; ) and an admixture model were adopted. STRUCTURE v. 2. 3. 1 is also able to make explicit use of sampling location information to provide accurate inferences , and the LOCPRIOR model was used. The likelihood of the assignments was evaluated for K varying from 1 to 9, and the reliability of the K clusters was tested for these values and variances among the trials; ΔK was also used to assess the true K number . In the F model, all K clusters are assumed to be diverged from a common ancestral population at the same time, but the model allows the possibility that the clusters may have experienced different degrees of drift since the divergence event to be considered . In this model, the amount of drift for each cluster from a common ancestral population is described as 'F', values of which are analogous to traditional FST values between clusters and a common ancestral population . Then, F for each cluster can be used as an indicator of genetic drift and/or bottleneck in the long term [44, 47]. F was assessed for each K to search for bottlenecks in the long term.
Allele polymorphisms and genotypic linkage
We were able to successfully genotype 605 individuals from 14 sampling sites using all five loci. No null alleles or scoring errors were detected by Micro-Checker. The number of alleles varied from 8 (Pq3422) to 16 (Pq3423), with an average of 12.4 alleles per locus. Nine of 150 tests [between Pq3003 and Pq3313 in 1 (NKH); Pq3313 and Pq3469, and Pq3422 and Pq3423 in 9 (SNN); Pq3313 and Pq3423, Pq3313 and Pq3469, and Pq3423 and Pq3469 in 10 (TRL); Pq3313 and Pq3469, Pq3422 and Pq3469, and Pq3422 and Pq3423 in 11 (TRT); P < 0.05] showed significant linkage disequilibrium after correcting for multiple tests.
Genetic diversity and Hardy-Weinberg equilibrium within populations and detection of bottleneck populations
Genetic variability estimates of Platypus quercivoru s samples
Sampling site and abbreviation
P value in BOTTLENECK
(Infinite allele mutation model, IAM)
Recent genetic bottlenecks were detected within sites 1 (NKH), 5 (BDU), 6 (TMG), 8 (SKE), 11 (TRT), and 12 (NGY; Table 2). Populations showing significant recent bottlenecks were distributed throughout north-eastern and south-western populations; however, they were all from newly emerged oak wilt forests for which the first damage occurred within the 2 years prior to sampling.
Population differentiation estimated FST (above the diagonal) and significance for each pairwise comparison (below the diagonal)
Hierarchical analysis of molecular variance (AMOVA) of Platypus quercivorus
source of variation
Sum of squares
Among collection year
ΦCT = 0.00593
Within collection year among sites
ΦSC = 0.04345
ΦST = 0.00593
Genetic diversity and population bottleneck
Our data indicate that high genetic diversity exists within populations regardless of the location or collection method. When samples from population 8 (SKE) were compared for their genetic diversity and components, the collection method (trapping flying adults or direct collection from trunks) had little effect on the overall results. Because no null alleles were detected, all of the genotyped data were considered useful for the analysis.
A bottleneck was detected in some populations from recently infested forests. Since there was no damage, these individuals may have originated as a small number of colonisers, which would have entailed a founder effect. If they were not colonisers, they may have been residents, in which case sudden changes in the availability of resources might have resulted in fluctuations in the population size of the beetle in damaged oak forests.
Linkage disequilibrium was also detected in recently infested forest area populations. The lack of linkage disequilibrium in most populations suggests that the loci used may not be in close proximity to one another on the chromosomes. Population bottlenecks and/or admixturing of differentiated populations (mentioned below) might be related to linkage disequilibrium in populations from recently infested areas.
Oak wilt damage expansion and P. quercivorus demographic history
The moderate level of population differentiation (FST = 0.047, G'ST = 0.167) detected in P. quercivorus suggests that gene flow among populations is balanced with genetic drift for the entire study area. Genetic analysis using microsatellite markers revealed two main genetic groups of P. quercivorus in Japan. These are divided into north-eastern and south-western populations. Using chloroplast DNA, Okaura et al.  examined the phylogeography of Q. crispula and three related species, which are the main hosts of P. quercivorus. Quang et al. [49, 50] showed nucleotide functional gene variation in Q. crispula and inferred its history of colonisation into Japan. These studies showed that a genetic boundary for Q. crispula was found between the north-eastern and south-western populations and coincides with the Itoigawa-Shizuoka Tectonic Line, a major tectonic depression that runs through Nagano Prefecture and separates sites 1 (NKH) through 9 (SNN) from 10 (TRL) through 14 (MMD). Given that the beetles exhibited the same pattern of genetic differentiation between the eastern and western populations, it is possible that they share a population history with Quercus species.
A change in the vegetation distribution would likely affect the beetle's genetic structuring in evolutionary timescales. Kamata et al.  predicted that P. quercivorus expanded its distribution northward and upward in elevation, and that the lack of co-evolution between Q. crispula and the beetle had an influence on the damage that incurred. They further proposed that a recent warmer climate has altered the distribution of P. quercivorus such that they encountered Q. crispula, which is lacking in defence systems against the disease. Given that the genetic structuring of P. quercivorus suggests that the species became established in accordance with the vegetation history on a regional scale, it is difficult to support Kamata et al.'s hypothesis  for the main cause of oak wilt expansion in entire regions. Although the genetic structures of the hosts and vectors coincide, the hypothesis that individuals from different origins invaded Japan and caused population structuring divided by the Itoigawa-Shizuoka Tectonic Line by chance cannot be rejected. Further details of the population history of the beetles may be elucidated by additional phylogenetic analysis using individuals from outside Japan.
A rapid expansion of species distribution often entails a lack of geographical structuring [2, 51]. The beetle populations that are damaging Q. crispula in north-eastern areas are not considered exclusively invasive populations because IBD was detected within the north-eastern populations. According to Rousset , most populations are thought to be in genetic equilibrium between genetic drift and gene flow. However, we identified a single population that may be composed of migrants. Specifically, the population at site 1 (NKH), which represents the northern limit of oak wilt in 2007, exhibited inbreeding, and an analysis using the STRUCTURE programme predicted that the population was a result of admixturing of two groups. Therefore, the damage at site 1 (NKH) may have been caused by non-local individuals from various source populations.
There was no visible damage near site 10 (TRL), and it was not clear whether the beetles therein that attacked Q. serrata were endemic or migrant. Adults were caught by trapping within site 11 (TRT), and there was no oak wilt. Site 11 (TRT) is near to site 10 (TRL). Beetle populations from sites 11 (TRT) and 10 (TRL) showed similar genetic compositions with high genetic diversity. Therefore, they may not represent descendants of long-distance dispersers from severely damaged areas, and may instead be endemic populations. The beetle populations within sites 10 (TRL) and 11 (TRT) must have persisted without a break.
Population expansion and migration of P. quercivorus
The NJ, SAMOVA and clustering by STRUCTURE analyses indicated that within the north-eastern populations, genetic components were substituted between 2005 and 2007. Because IBD was detected among north-eastern populations in 2005, most of the migration should be limited to neighbourhoods. This substitution may have been attained by gradual expansion of population distributions and mixing of population structures prior to the emergence of oak wilt damage. The migration of the beetles among populations might be enhanced by increased oak wilt damage. Therefore, samples collected in 2007 in north-eastern regions may contain individuals from broader areas. Although the beetles may seldom disperse long distances beyond a regional scale, migration within a regional scale may be frequent.
Chapuis et al.  examined population outbreak effects using the locust Locusta migratoria. They showed that population outbreaks reduce population differentiation by enhancing migration and/or effective population sizes. Thus, an increased P. quercivorus population size might alter the population structure in a similar manner to that seen with L. migratoria. Our STRUCTURE analysis produced an F value that indicated a high value for clustering of individuals in north-eastern populations mostly collected in 2005 when K = 3. However, the F value decreased in the 2007 collection cluster. This result indicates that the north-eastern population collected in 2005 was influenced by strong genetic drift after divergence from the common ancestral population, and that the effective population size is small. By incorporating independent demes, outbreak populations from 2005 to 2007 might have made the effective population sizes larger.
Implications for developing protection plans against oak wilt
The genetic structure of the beetles supported Kobayashi and Ueda's prediction  that changes in the management of oak forests are the main cause of the increase in oak wilt. Although migrant beetles might occasionally produce new areas of oak wilt damage, our analysis of the genetic structuring of the vector beetle populations indicated that the invasion of the beetle via long-distance dispersal into non-endemic areas or distribution change above regional scale may not be the main cause of oak wilt expansion. Outbreak populations might have enhanced migration on a small scale, and beetles in neighbouring populations may have encouraged such activity. Population outbreaks may also have led to mass attacks on host trees in neighbouring forests, thus establishing new damaged areas. The sequence of activation of the beetle might have promoted disastrous oak wilt.
To prevent the expansion of the disease to undamaged areas, it is important to manage beetle populations to maintain low densities and thus stopping the sequence of activation. Therefore, we need to trap beetles and/or monitor oak wilt damage to provide an indication of the population density of the beetles in continuous oak forests. Once damage is confirmed, the health of the oak forest can be maintained if immediate action is taken before Allee effects (in which the growth rate of sparse populations increases with increasing population density; ) become severe. In particular, in mature oak forests composed of plenty of large-diameter trees, vast numbers of beetles can emerge from a few wilted oaks . We must monitor oak forests that have been abandoned for a long time and are full of large trees with caution, so as not to release pests to nearby forests. Further studies of the dispersal dynamics of the beetles, such as frequency and distance of adult flight, are needed to generate alert systems for oak wilt expansion.
We found genetic differentiation in the ambrosia beetle P. quercivorus, which corresponds to the genetic structuring of the host oak trees. Based on a combination of methods of statistical analysis, the genetic structure is presented with a high degree of confidence. The geographical structuring of the beetle was thought to be formed by co-evolution with its host species.
We thank M. Kobayashi, T. Ono, H. Kinuura, and M. Tokoro for collection of samples and comments on the study. K. Hamaguchi kindly provided the primer sets. We also thank Y. Takahata for supplying figures of oak wilt damage distributions. This study was supported by the Research Project for Utilizing Advanced Technologies in Agriculture, Forestry and Fisheries No. 1775; Research and Development Projects for application in promoting new policies of Agriculture Forestry and Fisheries No. 2022 from the Research Council, Ministry of Agriculture, Forestry and Fisheries of Japan; and the program for Supporting Activities for Female Researchers fund No. 200003 by the Special Coordination Fund for Promoting Science and Technology of Ministry of Education, Culture, Sports, Science and Technology, Japan.
- Shoda-Kagaya E: Genetic differentiation of the pine wilt disease vector Monochamus alternatus over a mountain range - revealed from microsatellite DNA markers. Bull Entomol Res. 2007, 97: 167-174. 10.1017/S000748530700483X.View ArticlePubMedGoogle Scholar
- Dalmon A, Halkett F, Graier M, Delatte H, Peterschmitt M: Genetic structure of the invasive pest Bemisia tabaci: evidence of limited but persistent genetic differentiation in glasshouse populations. Heredity. 2008, 100: 316-325. 10.1038/sj.hdy.6801080.View ArticlePubMedGoogle Scholar
- Forestry Agency: Annual Report on Trends in Forests and Forestry. 2006, Tokyo, Japan Forestry AssociationGoogle Scholar
- Kubono T, Ito S: Raffaelea quercivora sp. nov. associated with mortality of Japanese oak, and the ambrosia beetle (Platypus quercivorus). Mycoscience. 2002, 43: 255-260. 10.1007/s102670200037.View ArticleGoogle Scholar
- Ito S, Kubono T, Sahashi N, Yamada T: Associated fungi with the mass mortality of oak trees. J Jpn For Soc. 1998, 80: 170-175. (in Japanese with English summary)Google Scholar
- Kinuura H, Kobayashi M: Death of Quercus crispula by inoculation with adult Platypus quercivorus (Coleoptera: Platypodidae). Appl Entomol Zool. 2006, 41: 123-128. 10.1303/aez.2006.123.View ArticleGoogle Scholar
- Kamata N, Esaki K, Kato K, Igeta Y, Wada K: Potential impact of global warming on deciduous oak dieback caused by ambrosia fungus carried by ambrosia beetle in Japan. Bull Entomol Res. 2002, 92: 119-126.PubMedGoogle Scholar
- Saito K: Outbreak of Crossotarus quercivorus. Forest Pests. 1959, 87: 101-102. (in Japanese)Google Scholar
- Yamazaki S: Emergence of Platypodidae damages in Asahi, Niigata. Forest Pests. 1978, 311: 28-30. (in Japanese)Google Scholar
- Ito S, Yamada T: Distribution and spread of mass mortality of oak trees. J Jpn For Soc. 1998, 80: 229-232. (in Japanese)Google Scholar
- Takahata Y: What is oak wilt?. Oak Wilt and Forest Health in Satoyama. Edited by: Kuroda K. 2008, Tokyo, Zenrinkyou, 25-44. (in Japanese)Google Scholar
- Ichihara Y, Masuya H, Shoda-Kagaya E, Kubono T: Relationship between the number of entry holes bored by Platypus quercivorus and wilt of oak trees in field conditions. Tohoku J For Sci. 2009, 14: 7-11. (in Japanese)Google Scholar
- Kamata N, Goto H, Komura R, Kubo M, Mikage M, Muramoto K: Recent oak wilt in Russia and Korea and prospects in further study for the disease. Chubu For Res. 2006, 54: 235-238. (in Japanese)Google Scholar
- Kobayashi M, Ueda A: Wilt disease of Fagaceae trees caused by Platypus quercivorus (Maruyama) (Coleoptera: Platypodidae) and the associated fungus: aim is to clarify the damage factor. J Jpn For Soc. 2005, 87: 435-450. (in Japanese with an English summary)View ArticleGoogle Scholar
- Nagano Prefecture: Occurrence of damage by Platypus quercivorus. 2005, Nagano, (in Japanese)Google Scholar
- Rousset F: Genetic approaches to the estimation of dispersal rates. Dispersal. Edited by: Colbert J, Danchin E, Dhondt AA, Nichols JD. 2001, Oxford, Oxford university press, 18-28.Google Scholar
- Loxdale HD, Lushai G: Molecular markers in entomology (review). Bull Entomol Res. 1998, 88: 577-600. 10.1017/S0007485300054250.View ArticleGoogle Scholar
- Chapuis MP, Loiseau A, Michalakis Y, Lecoq M, Franc A, Estoup A: Outbreaks, gene flow and effective population size in the migratory locust, Locusta migratoria: a regional-scale comparative survey. Mol Ecol. 2009, 18: 792-800. 10.1111/j.1365-294X.2008.04072.x.View ArticlePubMedGoogle Scholar
- Tokoro M, Kobayashi M, Saito S, Kinuura H, Nakashima T, Shoda-Kagaya E, Kashiwagi T, Tebayashi S, Kim CS, Mori K: Novel aggregation pheromone, (1S,4R)-p-menth-2-en-1-ol, of the ambrosia beetle, Platypus quercivorus (Coleoptera: Platypodidae). Bull FFPRI. 2007, 6: 49-57.Google Scholar
- De Lamballerie X, Zandott C, Vignoli C, Bollet C, de Micco P: A one-step microbial DNA extraction method using "Chelex 100" suitable for gene amplification. Res Microbiol. 1992, 143: 785-790. 10.1016/0923-2508(92)90107-Y.View ArticlePubMedGoogle Scholar
- Kawai M, Shoda-Kagaya E, Maehara T, Zhou Z, Lian C, Iwata R, Yamane A, Hogetsu T: Genetic structure of pine sawyer Monochamus alternatus (Coleoptera: Cerambycidae) populations in Northeast Asia: consequences of the spread of pine wilt disease. Environ Entomol. 2006, 35: 569-579. 10.1603/0046-225X-35.2.569.View ArticleGoogle Scholar
- Hamaguchi K, Kato K, Esaki K, Kamata N: Isolation and characterization of 10 new microsatellite loci in the ambrosia beetle Platypus quercivorus. J For Res.Google Scholar
- Brookfield JFY: A simple new method for estimating null allele frequency from heterozygote deficiency. Mol Ecol. 1996, 5: 453-455.View ArticlePubMedGoogle Scholar
- Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P: MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004, 4: 535-538. 10.1111/j.1471-8286.2004.00684.x.View ArticleGoogle Scholar
- Yeh FC, Yang RC, Boyle TJB, Ye ZH, Mao JX: POPGENE, the user-friendly shareware for population genetic analysis. 1997, University of Alberta, Canada, Molecular Biology and Biotechnology Centre, [http://www.ualberta.ca/~fyeh/]Google Scholar
- El Mousadik A, Petit RJ: High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L) Skeels] endemic to Morocco. Theor Appl Genet. 1996, 92: 832-839. 10.1007/BF00221895.View ArticlePubMedGoogle Scholar
- Raymond M, Rousset F: Genepop (version 1.2): population-genetics software for exact tests and ecumenicism. J Hered. 1995, 86: 248-249.Google Scholar
- Rice WR: Analyzing tables of statistical tests. Evolution. 1989, 43: 223-225. 10.2307/2409177.View ArticleGoogle Scholar
- Goudet J: FSTAT (ver. 1.2): a computer program to calculate F-statistics. J Hered. 1995, 86: 485-486.Google Scholar
- Weir BS, Cockerham CC: Estimating F-statistics for the analysis of population structure. Evolution. 1984, 38: 1358-1370. 10.2307/2408641.View ArticleGoogle Scholar
- Hedrick PW: A standardized genetic differentiation measure. Evolution. 2005, 59: 1633-1638.View ArticlePubMedGoogle Scholar
- Crawford NG: SMOGD: software for the measurement of genetic diversity. Mol Ecol Resources. 2010, 10: 556-557. 10.1111/j.1755-0998.2009.02801.x.View ArticleGoogle Scholar
- Cornuet JM, Luikart G: Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics. 1997, 144: 2001-2014.Google Scholar
- Schneider S, Roessli D, Excoffier L: ARLEQUIN ver. 3.01: a software for population genetics data analysis. 2006, Genetics and Biometry Laboratory, University of GenevaGoogle Scholar
- Nei M, Tajima F, Tateno Y: Accuracy of estimated phylogenic trees from molecular data. J Mol Evol. 1983, 19: 153-170. 10.1007/BF02300753.View ArticlePubMedGoogle Scholar
- Langella O: Populations 1.2.30: population genetic software (individuals or populations distances, phylogenetic trees). 2007, [http://bioinformatics.org/~tryphon/populations/]Google Scholar
- SPSS Inc: SYSTAT var. 9.01 statistics. 1998, Chicago, SPSS Science Marketing Department, SPSS IncGoogle Scholar
- Quinn GP, Keough MJ: Experimental Design and Data Analysis for Biologists. 2002, Cambridge: Cambridge University PressView ArticleGoogle Scholar
- Dupanloup I, Schneider S, Excoffier L: A simulated annealing approach to define the genetic structure of populations. Mol Ecol. 2002, 11: 2571-2581. 10.1046/j.1365-294X.2002.01650.x.View ArticlePubMedGoogle Scholar
- Rousset F: Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics. 1997, 145: 1219-1228.PubMed CentralPubMedGoogle Scholar
- Mantel N: The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27: 209-220.PubMedGoogle Scholar
- Peakall R, Smouse P: GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006, 6: 288-295. 10.1111/j.1471-8286.2005.01155.x.View ArticleGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.PubMed CentralPubMedGoogle Scholar
- Falush D, Stephens M, Pritchard JK: Inference of population structure: extensions to linked loci and correlated allele frequencies. Genetics. 2003, 164: 1567-1587.PubMed CentralPubMedGoogle Scholar
- Hubisz M, Falush D, Stephens M, Pritchard J: Inferring weak population structure with the assistance of sample group information. Mol Ecol Resources. 2009, 9: 1322-1332. 10.1111/j.1755-0998.2009.02591.x.View ArticleGoogle Scholar
- Evanno G, Regnaut S, Goudet J: Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005, 14: 2611-2620. 10.1111/j.1365-294X.2005.02553.x.View ArticlePubMedGoogle Scholar
- Tsuda Y, Kimura M, Kato S, Katsuki T, Mukai Y, Tsumura Y: Genetic structure of Cerasus jamasakura, a Japanese flowering cherry, revealed by nuclear SSRs: implications for conservation. J Plant Res. 2009, 122: 367-375. 10.1007/s10265-009-0224-x.View ArticlePubMedGoogle Scholar
- Okaura T, Quang ND, Ubukata M, Harada H: Phylogeographic structure and late Quaternary population history of the Japanese oak Quercus mongolica var. crispula and related species revealed by chloroplast DNA variation. Genes Genet Syst. 2007, 82: 465-477. 10.1266/ggs.82.465.View ArticlePubMedGoogle Scholar
- Quang ND, Ikeda S, Harada K: Nucleotide variation in Quercus crispula Blume. Heredity. 2008, 101: 166-174. 10.1038/hdy.2008.42.View ArticlePubMedGoogle Scholar
- Quang ND, Ikeda S, Harada K: Patterns of nucleotide diversity at the methionine synthase locus in fragmented and continuous populations of a wind-pollinated tree, Quercus mongolica var. crispula. J Hered. 2009, 100: 762-770. 10.1093/jhered/esp036.View ArticlePubMedGoogle Scholar
- Tsutsui ND, Suarez AV, Holway DA, Case TJ: Relationships among native and introduced populations of the Argentine ant (Linepithema humile) and the source of introduced populations. Mol Ecol. 2001, 10: 2151-2161. 10.1046/j.0962-1083.2001.01363.x.View ArticlePubMedGoogle Scholar
- Drake JM: Allee effects and the risk of biological invasion. Risk Anal. 2004, 24: 795-802. 10.1111/j.0272-4332.2004.00479.x.View ArticlePubMedGoogle Scholar
- Kobayashi M: Vector of oak wilt, Platypus quercivorus. Interesting Life of Insects in the Trees: Invitation to Boring Insect Study. Edited by: Shibata M, Togashi K. 2006, Tokyo, Tokai Univ. Press, 89-212. (in Japanese)Google Scholar
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