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.基于限制性酶切位点相关DNA测序（restriction-site associated DNA sequencing, RADseq）生成的大型基因组数据集，结合种群历史推断方法，正不断提升我们解析物种种群历史的能力。本研究采用模拟方法，探究了RADseq数据集在种群数量稳定及下降趋势下，准确估算有效种群大小（effective population size, Ne）的潜力，并对比了两种分析方法在当代时间尺度（20个世代）内准确区分稳定种群与持续下降种群的能力。基于连锁不平衡的分析方法显示，个体采样量（即n≥30）对Ne估算与种群数量下降的检测效果影响最大，在种群下降启动约10个世代后，即可在各类模拟场景中可靠检测到下降信号。基于溯祖理论的推断方法则需要更少的采样个体（即n=15），其效果主要受单核苷酸多态性（single nucleotide polymorphism, SNPs）数据集规模的影响：若要准确检测种群趋势，需要25000至50000个SNPs，且需在种群下降启动至少20个世代后才能实现。在两种方法间进行选择时，可用样本量与预设的RADseq位点数量均为重要考量标准。两类方法均未因RAD数据典型的等位基因缺失效应出现明显偏差。在充分了解这些方法的局限性与偏差后，研究人员在设计采样方案与分析流程时可做出更合理的决策。总体而言，本研究结果表明，基于RADseq数据的种群历史推断可成功应用于近期种群数量变化的解析，或可成为种群监测与保护生物学领域的重要工具。