LAUSR

From its inception, population genetics has been nearly as concerned with the genetic data type—to which analyses are brought to bear—as it is with the analysis methods themselves. The field has traversed allozymes, microsatellites, segregating sites in multilocus alignments and, currently, single nucleotide polymorphisms (SNPs) generated by high‐throughput genomic sequencing methods, primarily whole genome sequencing and reduced representation library (RRL) sequencing. As each emerging data type has gained traction, it has been compared to existing methods, based on its relative ability to discern population structural complexity at increasing levels of resolution. However, this is usually done by comparing the gold standard in one data type to the gold standard in the new data type. These gold standards frequently differ in power and in sampling density, both across a genome and throughout a spatial range. In this issue of Molecular Ecology, D’Aloia et al. apply the high‐throughput approach as fully as possible to microsatellites, nuclear loci and SNPs genotyped through an RRL method; this is coupled with a spatially dense sampling scheme. Completing a battery of population genetics analyses across data types (including a series of down‐sampled data sets), the authors find that SNP data are slightly more sensitive to fine‐scale genetic structure, and the results are more resilient to down‐sampling than microsatellites and nonrepetitive nuclear loci. However, their results are far from an unqualified victory for RRL SNP data over all previous data types: the authors note that modest additions to the microsatellites and nuclear loci data sets may provide the necessary analytical power to delineate the fine‐scale genetic structuring identified by SNPs. As always, as the field begins to fully embrace the newest thing, good science reminds us that traditional data types are far from useless, especially when combined with a well‐designed sampling scheme.