Data from: Estimation of contemporary effective population size and population declines using RAD sequence data

Large genomic datasets generated with restriction-site associated DNA
sequencing (RADseq), in combination with demographic inference methods,
are improving our ability to gain insights into the population history of
species. We used a simulation approach to examine the potential for RADseq
datasets to accurately estimate effective population size (Ne) over the
course of stable and declining population trends, and we compare the
ability of two methods of analysis to accurately distinguish stable from
steadily declining populations over a contemporary time scale (20
generations). Using a linkage disequilibrium-based analysis, individual
sampling (i.e., n ≥ 30) had the greatest effect on Ne estimation and the
detection of population-size declines, with declines reliably detected
across scenarios approximately 10 generations after they began.
Coalescent-based inference required fewer sampled individuals (i.e., n =
15), and instead was most influenced by the size of the SNP dataset, with
25,000 to 50,000 SNPs required for accurate detection of population trends
and at least 20 generations after decline began. The number of samples
available and targeted number of RADseq loci are important criteria when
choosing between these methods. Neither method suffered any apparent bias
due to the effects of allele dropout typical of RAD data. With an
understanding of the limitations and biases of these approaches,
researchers can make more informed decisions when designing their sampling
and analyses. Overall, our results reveal that demographic inference using
RADseq data can be successfully applied to infer recent population size
change and may be important tools for population monitoring and
conservation biology.