Data from: The effect of neighborhood size on effective population size in theory and in practice

The distinction between the effective size of a population (Ne) and the effective size of its neighborhoods (Nn) has sometimes become blurred. Ne reflects the effect of random sampling on the genetic composition of a population of size N, whereas Nn is a measure of within-population spatial genetic structure and depends strongly on the dispersal characteristics of a species. Although Nn is independent of Ne, the reverse is not true. Using simulations of a population of annual plants, it was found that the effect of Nn on Ne was well approximated by Ne=N/(1−FIS), where FIS (determined by Nn) was evaluated population wide. Nn only had a notable influence of increasing Ne as it became smaller (less than or equal to16). In contrast, the effect of Nn on genetic estimates of Ne was substantial. Using the temporal method (a standard two-sample approach) based on 1000 single-nucleotide polymorphisms (SNPs), and varying sampling method, sample size (2–25% of N) and interval between samples (T=1–32 generations), estimates of Ne ranged from infinity to <0.1% of the true value (defined as Ne based on 100% sampling). Estimates were never accurate unless Nn and T were large. Three sampling techniques were tested: same-site resampling, different-site resampling and random sampling. Random sampling was the least biased method. Extremely low estimates often resulted when different-site resampling was used, especially when the population was large and the sample fraction was small, raising the possibility that this estimation bias could be a factor determining some very low Ne/N that have been published.种群有效大小（Ne）与邻域有效大小（Nn）之间的界定有时会变得模糊不清。Ne反映了随机抽样对大小为N的种群遗传组成的影响，而Nn则是衡量种群内空间遗传结构的指标，且强烈依赖于物种的扩散特征。尽管Nn不受Ne的影响，但反之则不成立。通过对一年生植物种群的模拟实验发现，Nn对Ne的影响可通过公式Ne=N/(1−FIS)较好地近似，其中FIS（由Nn决定）是在全种群水平上评估得到的。仅当Nn变小（≤16）时，其对Ne的提升作用才较为显著。与之相反，Nn对Ne遗传估计值的影响则十分显著。基于1000个单核苷酸多态性（SNPs）的时间法（一种标准双样本方法），通过改变抽样方法、样本量（为种群大小N的2%~25%）以及样本间隔（T=1~32代），得到的Ne估计值范围从无穷大到真实值（以100%抽样得到的Ne为基准定义）的0.1%以下。除非Nn与T取值较大，否则Ne的估计值永远无法准确。研究共测试了三种抽样技术：同位点重抽样、异位点重抽样以及随机抽样。随机抽样是偏差最小的方法。采用异位点重抽样时，往往会得到极低的Ne估计值，尤其是在种群规模较大且抽样比例较小时，这表明该估计偏差可能是导致已发表文献中出现部分极低Ne/N比值的原因之一。