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Across the tree of life, many organisms are able to reproduce clonally, via vegetative spread, budding or parthenogenesis. In population genetic analyses of clonally reproducing organisms, it is common practice to retain only a single representative per multilocus genotype. Though this practice of clone correction is widespread, the theoretical justification behind it has been very little studied. Here, I use individual-based simulations to study the effect of clone correction on the estimation of the genetic summary statistics H O , H S , F IS , F ST , F'' ST and D est . The simulations follow the standard finite island model, consisting of a set of populations connected by gene flow, but with a variable rate of sexual versus asexual reproduction. The results of the simulations show that by itself, the inclusion of replicated genotypes does not lead to a deviation in the values of the summary statistics, except when the rate of sexual reproduction is less than about one in thousand. However, clone correction can introduce a strong deviation in the values of most of the statistics, when compared to a scenario of full sexual reproduction. For H S and F IS , this deviation can be informative about the process of asexual reproduction, but for F ST , F'' ST and D est , clone correction can lead to incorrect conclusions. I therefore argue that clone correction is not strictly necessary, but can in some cases be insightful. However, when clone correction is applied, it is imperative that results for both the corrected and uncorrected data are presented., (© 2024 The Author(s). Molecular Ecology Resources published by John Wiley & Sons Ltd.)

genodive version 3.0 is a user-friendly program for the analysis of population genetic data. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation (φ ST , F ST , F' ST , G ST , G' ST , G'' ST , D est , R ST , ρ), analysis of molecular variance-based K-means clustering, Hardy-Weinberg equilibrium, hybrid index, population assignment, clone assignment, Mantel test, Spatial Autocorrelation, 23 ways of calculating genetic distances, and both principal components and principal coordinates analyses. A unique feature of genodive is that it can also open data sets with nongenetic variables, for example environmental data or geographical coordinates that can be included in the analysis. In addition, genodive makes it possible to run several external programs (lfmm, structure, instruct and vegan) directly from its own user interface, avoiding the need for data reformatting and use of the command line. genodive is available for computers running Mac OS X 10.7 or higher and can be downloaded freely from: http://www.patrickmeirmans.com/software., (© 2020 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.)

The estimation of migration rates using molecular markers is an important aspect of many population genetic studies. Several different methods are available for estimating migration, but most of these make multiple limiting assumptions. One method that is relatively free from assumptions is BAYESASS, which uses assignment methods in a Bayesian framework. However, when tested using simulated data, this method was found to have problems with the convergence of the Markov chain Monte Carlo. Here, I perform a literature study to test whether these convergence problems are also present when BAYESASS is used to estimate migration rates from empirical data. A review of 100 studies that have used BAYESASS shows that this is indeed the case. The estimated proportions of nonmigrants were mostly either close to 2/3 or 1, indicating that the MCMC tends to get trapped near the bounds of the prior distribution. In addition, I found that the quality of the inference was negatively affected by the number of sampled populations, but increased with increasing numbers of sampled individuals and with the strength of the population structure as measured by FST . Based on these results, I give several recommendations that should help to reduce problems when using BAYESASS with empirical data. Most importantly, I argue that researchers should be more realistic about inferences of migration rates and that BAYESASS will give optimal results only when the experiment has been especially designed around its use., (© 2013 John Wiley & Sons Ltd.)

MARLIN is a software to create, run, analyse, and visualize spatially explicit population genetic simulations. It provides an intuitive user interface with which the geographical layout of a metapopulation can be drawn by hand or loaded from a map. Furthermore, the interface allows easy selection of the many different simulation settings. MARLIN then uses the program QuantiNemo to run the simulation in the background. When simulations are finished, MARLIN directly analyses and plots the results, thereby greatly simplifying the simulation workflow. This combination of simulation and analysis makes MARLIN ideal for teaching and for scientists who are interested in doing simulations without having to learn command-line operations. MARLIN is available for computers running Mac OS X and can be downloaded from: http://www.patrickmeirmans.com/software., (© 2010 Blackwell Publishing Ltd.)

Although F(ST) is widely used as a measure of population structure, it has been criticized recently because of its dependency on within-population diversity. This dependency can lead to difficulties in interpretation and in the comparison of estimates among species or among loci and has led to the development of two replacement statistics, F'(ST) and D. F'(ST) is the normal F(ST) standardized by the maximum value it can obtain, given the observed within-population diversity. D uses a multiplicative partitioning of diversity, based on the effective number of alleles rather than on the expected heterozygosity. In this study, we review the relationships between the three classes of statistics (F(ST), F'(ST) and D), their estimation and their properties. We illustrate the relationships between the statistics using a data set of estimates from 84 species taken from the last 4 years of Molecular Ecology. As with F(ST), unbiased estimators are available for the two new statistics D and F'(ST). Here, we develop a new unbiased F'(ST) estimator based on G(ST), which we call G''(ST). However, F'(ST) can be calculated using any F(ST) estimator for which the maximum value can be obtained. As all three statistics have their advantages and their drawbacks, we recommend continued use of F(ST) in combination with either F'(ST) or D. In most cases, F'(ST) would be the best choice among the latter two as it is most suited for inferences of the influence of demographic processes such as genetic drift and migration on genetic population structure., (© 2010 Blackwell Publishing Ltd.)