SNP dataset for the threatened plant species Dinizia jueirana-facao (Fabaceae)

By performing sensitivity analyses, we empirically investigated how
decisions about the percentage of missing data (MD) and the minor allele
frequency (MAF) set in bioinformatic processing of genomic data affect
direct (i.e., parentage analysis) and indirect (i.e., fine-scale spatial
genetic structure - SGS) gene flow estimates. We focus specification on
these manifestations in small plant populations, and specifically, in the
rare tropical plant species Dinizia jueirana-facao, where assumptions
implicit to analytical procedures for accurate estimates of gene flow may
not hold. Avoiding biases in dispersal estimates are essential given this
species is facing extinction risks due to habitat loss, and so we also
investigate the effects of forest fragmentation on the accuracy of
dispersal estimates under different filtering criteria by testing for
recent decrease in the scale of gene flow. Our sensitivity analyses
demonstrate that gene flow estimates are robust to different setting of
MAF (0.05 to 0.35) and MD (0 to 20%). Comparing the direct and indirect
estimates of dispersal, we find that contemporary estimates of gene
dispersal distance (σrt = 41.8 m) was ~ fourfold smaller than the
historical estimates, supporting the hypothesis of a temporal shift in the
scale of gene flow in D. jueirana-facao, which is consistent with
predictions based on recent, dramatic forest fragmentation process. While
we identified settings for filtering genomic data to avoid biases in gene
flow estimates, we stress that there is no ‘rule of thumb’ for
bioinformatic filtering or that relying on default program settings is
advisable. Instead, we suggest that the approach implemented here be
applied independently in each separate empirical study to confirm
appropriate settings to obtain unbiased population genetics estimates.