This is particularly useful for organisms such as parasites and vectors that are difficult to observe directly even with mark–release–recapture methods. The ecology of natural populations can conveniently be assessed through the study of spatio-temporal genetic variations of molecular markers with population genetics tools. We finally use our conclusions for reanalyzing and reinterpreting some published data sets. 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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