About: Molecular Ecology is an academic journal published by Wiley-Blackwell. The journal publishes majorly in the area(s): Population & Biological dispersal. It has an ISSN identifier of 0962-1083. Over the lifetime, 10408 publications have been published receiving 785432 citations.
Papers published on a yearly basis
TL;DR: It is found that in most cases the estimated ‘log probability of data’ does not provide a correct estimation of the number of clusters, K, and using an ad hoc statistic ΔK based on the rate of change in the log probability between successive K values, structure accurately detects the uppermost hierarchical level of structure for the scenarios the authors tested.
Abstract: 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.
TL;DR: In this paper, two taxon-selective primers for the internal transcribed spacer (ITS) region in the nuclear ribosomal repeat unit were proposed, which were intended to be specific to fungi and basidiomycetes, respectively.
Abstract: We have designed two taxon-selective primers for the internal transcribed spacer (ITS) region in the nuclear ribosomal repeat unit. These primers, ITS1-F and ITS4-B, were intended to be specific to fungi and basidiomycetes, respectively. We have tested the specificity of these primers against 13 species of ascomycetes, 14 of basidiomycetes, and 15 of plants. Our results showed that ITS4-B, when paired with either a 'universal' primer ITS1 or the fungal-specific primer ITS1-F, efficiently amplified DNA from all basidiomycetes and discriminated against ascomycete DNAs. The results with plants were not as clearcut. The ITS1-F/ITS4-B primer pair produced a small amount of PCR product for certain plant species, but the quantity was in most cases less than that produced by the 'universal' ITS primers. However, under conditions where both plant and fungal DNAs were present, the fungal DNA was amplified to the apparent exclusion of plant DNA. ITS1-F/ITS4-B preferential amplification was shown to be particularly useful for detection and analysis of the basidiomycete component in ectomycorrhizae and in rust-infected tissues. These primers can be used to study the structure of ectomycorrhizal communities or the distribution of rusts on alternate hosts.
TL;DR: This paper showed that the likelihood equations used by versions 1.0 and 2.0 of CERVUS to accommodate genotyping error miscalculate the probability of observing an erroneous genotype.
Abstract: Genotypes are frequently used to identify parentage. Such analysis is notoriously vulnerable to genotyping error, and there is ongoing debate regarding how to solve this problem. Many scientists have used the computer program CERVUS to estimate parentage, and have taken advantage of its option to allow for genotyping error. In this study, we show that the likelihood equations used by versions 1.0 and 2.0 of CERVUS to accommodate genotyping error miscalculate the probability of observing an erroneous genotype. Computer simulation and reanalysis of paternity in Rum red deer show that correcting this error increases success in paternity assignment, and that there is a clear benefit to accommodating genotyping errors when errors are present. A new version of CERVUS (3.0) implementing the corrected likelihood equations is available at http://www.fieldgenetics.com.
TL;DR: This study derives likelihood ratios for paternity inference with codominant markers taking account of typing error, and defines a statistic Δ for resolving paternity, and demonstrates the method is robust to their presence under commonly encountered conditions.
Abstract: Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic delta for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for delta that permit assignment of paternity to the most likely male with a known level of statistical confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.