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ABSTRACT 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 HO, HS, FIS, FST, F′′ST and Dest. 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 HS and FIS, this deviation can be informative about the process of asexual reproduction, but for FST, F′′ST and Dest, 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. [ABSTRACT FROM AUTHOR]

Understanding the impact of historical and demographic processes on genetic variation is essential for devising conservation strategies and predicting responses to climate change. Recolonization after Pleistocene glaciations is expected to leave distinct genetic signatures, characterised by lower genetic diversity in previously glaciated regions. Populations' positions within species ranges also shape genetic variation, following the central‐marginal paradigm dictating that peripheral populations are depauperate, sparse and isolated. However, the general applicability of these patterns and relative importance of historical and demographic factors remains unknown. Here, we analysed the distribution of genetic variation in 91 native species of North American plants by coupling microsatellite data and species distribution modelling. We tested the contributions of historical climatic shifts and the central‐marginal hypothesis on genetic diversity and structure on the whole data set and across subsets based on taxonomic groups and growth forms. Decreased diversity was found with increased distance from potential glacial refugia, coinciding with the expected make‐up of postglacially colonised localities. At the range periphery, lower genetic diversity, higher inbreeding levels and genetic differentiation were reported, following the assumptions of the central‐marginal hypothesis. History and demography were found to have approximately equal importance in shaping genetic variation. [ABSTRACT FROM AUTHOR]

Analyzing autopolyploid genetic data still presents numerous challenges due to, e.g., missing dosage information of genotypes and the presence of multiple ploidy levels within species or populations, but also because the choice of software is limited when compared to what is available for diploid data. However, over the last years, the number of software programs that can deal with polyploid data is slowly increasing. The software GENODIVE is one of the most widely used programs for the analysis of polyploid genetic data, presenting a wide array of different methods. In this chapter, I outline several frequently used types of population genetic analyses and explain how these apply to polyploid data, including possible pitfalls and biases. I then explain how GENODIVE approaches these analyses and whether and how it can overcome possible biases. Specifically, I focus on analyses of genetic diversity, Hardy-Weinberg equilibrium, quantifying population differentiation, clustering, and calculation of genetic distances. GENODIVE can be downloaded freely from http://www.patrickmeirmans.com/software ., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)