Considerations for measuring genetic variation and population structure with multilocus fingerprinting

Multilocus DNA fingerprinting provides a cost-effective means to rapidly assay genetic variation at many loci. While this makes the technique particularly attractive for studies of evolution and conservation biology, fingerprint data can be difficult to interpret. Measurement errors inherent with the technique force investigators to group similar-sized alleles (bands) into discrete bins before estimating genetic parameters. If too little error is accounted for in this process homologous alleles will not be grouped in a common bin, whereas overestimated error can produce bins With homoplasic alleles. We used simulations and empirical data for two frog species (Rana luteiventris and Hyla regilla) to demonstrate that mean band-sharing ((S) over bar(xy)) and heterozygosity ((H) over bar(E)) are a function of both bin width and band profile complexity (i.e. number and distribution of bands). These estimators are also sensitive to the number of lanes included in the analysis when bin width is wide and a floating bin algorithm is employed. Multilocus estimates of (H) over bar(E), were highly correlated with (S) over bar(xy) and thus provide np additional information about genetic variation. Estimates of population subdivision ((F) over cap and <(Phi)over cap>(ST)) appeared robust to changes in bin size. We also examined the issue of statistical independence for band-sharing data when comparisons are made among all samples. This analysis indicated that the covariance between band-sharing statistics was very small and not statistically different from zero. We recommend that sensitivity analyses for bin size be used to improve confidence in the biological interpretation of multilocus fingerprints, and that the covariance structure for band-sharing statistics be examined.