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Population structure of the butternut canker fungus, Ophiognomonia clavigignenti-juglandacearum, in North American forests

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      Abstract

      The occurrence of multiple introduction events, or sudden emergence from a host jump, of forest pathogens may be an important factor in successful establishment in a novel environment or on a new host; however, few studies have focused on the introduction and emergence of fungal pathogens in forest ecosystems. While Ophiognomonia clavigignenti-juglandacearum (Oc-j), the butternut canker fungus, has caused range-wide mortality of butternut trees in North America since its first observation in 1967, the history of its emergence and spread across the United States and Canada remains unresolved. Using 17 single nucleotide polymorphic loci, we investigated the genetic population structure of 101 isolates of Oc-j from across North America. Clustering analysis revealed that the Oc-j population in North America is made up of three differentiated genetic clusters of isolates, and these genetic clusters were found to have a strong clonal structure. These results, in combination with the geographic distribution of the populations, suggest that Oc-j was introduced or has emerged in North America on more than one occasion, and these clonal lineages have since proliferated across much of the range of butternut. No evidence of genetic recombination was observed in the linkage analysis, and conservation of the distinct genetic clusters in regions where isolates from two or more genetic clusters are present, would indicate a very minimal or non-existent role of sexual recombination in populations of Oc-j in North America.

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      Most cited references 67

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      CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

      The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
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        Inference of population structure using multilocus genotype data.

        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.
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          Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

          The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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            Author and article information

            Affiliations
            [1 ]Department of Biological Sciences, University of New Hampshire 46 College Rd, Durham, New Hampshire, 03824
            [2 ]School of Environmental Sciences, University of Guelph Guelph, Ontario, Canada, N1G 2W1
            Author notes
            Kirk D. Broders, Department of Biological Sciences, University of New Hampshire, 46 College Rd., Durham, NH 03824. Tel: +00 603 862-4542; Fax: +00 603 862-3784; E-mail: kirk.broders@ 123456unh.edu

            Funding Information This Project was funded by Ontario Ministry of Natural Resources (OMNR), Natural Sciences and Engineering Research Council (NSERC) of Canada, the National Geographic Society Committee for Research and Exploration, and the New Hampshire Agriculture Experiment Station (NHAES).

            Journal
            Ecol Evol
            Ecol Evol
            ece3
            Ecology and Evolution
            Blackwell Publishing Ltd
            2045-7758
            2045-7758
            September 2012
            24 July 2012
            : 2
            : 9
            : 2114-2127
            23139872
            3488664
            10.1002/ece3.332
            © 2012 Published by Blackwell Publishing Ltd.

            Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

            Categories
            Original Research

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