Population structure of the beetle pests Phyllodecta vulgatissima and P. vitellinae on UK willow plantations.
ABSTRACT Phyllodecta (= Phratora) vulgatissima and P. vitellinae (Coleoptera: Chrysomelidae) are important pests of willows and poplars. Their differences in host species preference may provide a non-chemical control strategy for pest control. However, little is known about population structure with respect to hosts, regions or seasons. Using five microsatellites, 850 P. vulgatissima and 1100 P. vitellinae individuals, comprising 17 and 22 UK samples, respectively, were genotyped. High diversity was observed at all loci. Migrant numbers exchanged per generation (Nm) were high (2.1-12.6 for P. vulgatissima and 0.9-12.2 for P. vitellinae), suggesting high genetic exchange between samples. Estimates of population differentiation (FST) and analyses of the data using Bayesian methods (Partition and Structure) showed little evidence of subdivision in relation to geography, sampling time or host.
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ABSTRACT: Predation by the red fox Vulpes vulpes is believed to be threatening the little penguin Eudyptula minor on Phillip Island in Victoria. Polymorphism at seven microsatellite loci was examined to estimate the extent of differentiation between Phillip Island and mainland populations of V. vulpes. Loss of alleles has occurred on Phillip Island where foxes first appeared approximately 88 years ago compared with mainland populations. Genetic differentiation between the Phillip Island and mainland populations was high. The relatively high differentiation found between the two populations could be due to either low migration rates, the effect of the composition of founder animals or both effects. Further ecological and historical information about the populations is needed to explore the likely significance of these effects.Molecular Ecology 03/1996; 5(1):81-7. · 6.28 Impact Factor
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ABSTRACT: We present likelihood-based methods for assigning the individuals in a sample to source populations, on the basis of their genotypes at co-dominant marker loci. The source populations are assumed to be at Hardy-Weinberg and linkage equilibrium, but the allelic composition of these source populations and even the number of source populations represented in the sample are treated as uncertain. The parameter of interest is the partition of the set of sampled individuals, induced by the assignment of individuals to source populations. We present a maximum likelihood method, and then a more powerful Bayesian approach for estimating this sample partition. In general, it will not be feasible to evaluate the evidence supporting each possible partition of the sample. Furthermore, when the number of individuals in the sample is large, it may not even be feasible to evaluate the evidence supporting, individually, each of the most plausible partitions because there may be many individuals which are difficult to assign. To overcome these problems, we use low-dimensional marginals (the 'co-assignment probabilities') of the posterior distribution of the sample partition as measures of 'similarity', and then apply a hierarchical clustering algorithm to identify clusters of individuals whose assignment together is well supported by the posterior distribution. A binary tree provides a visual representation of how well the posterior distribution supports each cluster in the hierarchy. These methods are applicable to other problems where the parameter of interest is a partition of a set. Because the co-assignment probabilities are independent of the arbitrary labelling of source populations, we avoid the label-switching problem of previous Bayesian methods.Genetics Research 09/2001; 78(1):59-77. · 2.00 Impact Factor
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ABSTRACT: We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.Genetics 07/2000; 155(2):945-59. · 4.39 Impact Factor