![]() These results indicate the importance of estimating genetic differences under the model of sequence evolution that includes insertions and deletions in addition to substitutions. The genetic difference measure based on the K2P model, compared to our model, overestimates 21 sequence pairs that are not originally identical. Additionally, the numbers of sequence pairs with interspecific genetic differences of zero are 50 for the K2P model and 29 for our model. We observe that the percentage of interspecific sequence pairs with values less than the maximum intraspecific genetic difference is 43.2% for the K2P model which is calculated by removing gap sites across all sequences, 22.7% for the K2P model which is calculated by removing gap sites for sequence pairs, and 16.9% for our model which is calculated without removing gap sites. It is especially noteworthy that the use of different models affects the degree of overlap between intraspecific and interspecific genetic differences. Using the nuclear ribosomal DNA internal transcribed spacer 2 region from the genus Physalis, we demonstrate that species identification and phylogenetic studies strongly depend on evolutionary models. Here we extend the Kimura two-parameter (K2P) model by considering gaps (insertions and/or deletions) and introduce a new measure for estimating genetic difference between two nucleotide sequences in terms of nucleotide changes that have occurred during the evolutionary process. We finally use our conclusions for reanalyzing and reinterpreting some published data sets.Accurate estimates of genetic difference are required for research in evolutionary biology. Nevertheless, Chapuis and Estoup's FreeNA correction for null alleles provides very good results in most situations. This can have important consequences on inferences that can be made from such data. We also show that the proportion of null allelic states interact with the slope of the regression of F ST/(1-F ST) as a function of geographic distance. When null alleles are introduced, the power of detection of isolation by distance is significantly reduced and D CSE remains the most powerful genetic distance. Metapopulations composed of small sub-population numbers thus display smaller neighborhood sizes. Marginal sub-populations behave as smaller neighborhoods. Nevertheless, for markers with genetic diversities H S<0.4-0.5, all statistics tend to display the same statistical power. In stepping stone models without null alleles, the best statistic to detect isolation by distance in most situations is the chord distance D CSE. Impact of null alleles of increasing frequency is also studied. In this simulation study, we analyze the behavior of different genetic distances in Island (null hypothesis) and stepping stone models displaying varying neighborhood sizes. These problems can alter population parameter inferences that can be extracted from molecular data. Molecular markers can often display technical caveats, such as PCR-based amplification failures (null alleles, allelic dropouts). To analyze isolation by distance from molecular data, one can use some kind of genetic distance or coalescent simulations. Studying isolation by distance can provide useful demographic information.
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