θ, G ST) suffer from an incompatibility when applied to highly polymorphic microsatellite markers (Hedrick 1999 Jost 2008), as a result of their negative dependence on within subpopulation heterozygosity (Jost 2008). For instance, F-statistics is a commonly used framework for the description of genetic diversity partitioning within and among populations. Additional online resources for the package are also described, including a GUI web app version intended for those with more limited experience using R for statistical analysis.Īs a consequence of the growing suite of statistical genetics tools, which are often tailored to particular marker types, the analyses of population genetic data are becoming an increasingly complex task (Excoffier & Heckel 2006). The examples demonstrate useful strategies for the exploration of data and interpretation of results generated by diveRsity. Detailed examples of usage and descriptions of package capabilities are provided.However, the package can be readily used for other co-dominant marker types (e.g. diveRsity has a set of unique features, which facilitate the use of an informed framework for assessing the validity of the use of traditional F-statistics for the inference of demography, with reference to specific marker types, particularly focusing on highly polymorphic microsatellite loci.Various plotting tools are also provided for a visual evaluation of estimated values, allowing users to critically assess the validity and significance of statistical tests from a biological perspective. The package calculates these estimators along with their respective bootstrapped confidence intervals for loci, sample population pairwise and global levels. We present a new R package, diveRsity, for the calculation of various diversity statistics, including common diversity partitioning statistics (θ, G ST) and population differentiation statistics (D Jost,, χ 2 test for population heterogeneity), among others.
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