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, while Nn is a measure of within-population spatial genetic structure and depends strongly on the dispersal characteristics of a species. While 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 (≤16). 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 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 re-sampling; different-site re-sampling; and random sampling. Random sampling was the least biased method. Extremely low estimates often resulted when different-site re-sampling 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（effective population size，Ne）与其邻域有效大小（neighborhood effective size，Nn）之间的界限有时会变得模糊。Ne反映了随机抽样对规模为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性。尽管Nn独立于Ne，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的影响可通过近似公式Ne=N/(1-FIS)得到较好的近似，其中FIS（由Nn决定）需在全种群范围内进行评估。仅当Nn变得足够小（≤16）时，其对Ne的提升作用才会较为显著。与之相反，Nn对Ne的遗传估计值的影响则十分显著。基于1000个单核苷酸多态性（Single Nucleotide Polymorphisms，SNPs）的时间法（一种标准双样本分析方法），通过改变抽样方法、样本量（为种群规模N的2%~25%）以及样本采集间隔世代数（T=1~32代），得到的Ne估计值范围从无穷大到真实Ne值的0.1%以下（真实Ne值定义为基于100%抽样得到的Ne）。除非Nn与T均足够大，否则Ne的估计值始终无法准确。研究共测试了三种抽样技术：同一样点重抽样、不同样点重抽样以及随机抽样。其中随机抽样法的偏倚程度最低。当采用不同样点重抽样时，常会得到极低的Ne估计值，尤其在种群规模较大且样本占比偏小时，这提示这种估计偏倚可能是导致部分已发表的Ne/N比值极低现象的潜在因素之一。