Inbreeding depression and drift load in small populations at demographic disequilibrium
Inbreeding depression and drift load in small populations at demographic disequilibrium
复制标题
人口不平衡时小种群的近交衰退和漂流负荷
作者：
Rachel B Spigler;K. Theodorou;Shu
近亲繁殖衰退是交配系统进化的主要驱动力，对种群生存能力具有重要意义。理论和经验的注意力已经支付到预测近交衰退如何随着人口规模的变化。较低的近交衰退预测在小种群平衡，主要是由于较高的近亲繁殖率促进净化和/或固定有害等位基因（漂移负荷），但在人口和遗传不平衡的预测不太清楚。在这项研究中，我们的实验评估如何终身近交抑郁症和漂移负荷，估计杂种优势，不同的人口普查（NC）和有效的（估计为遗传多样性，他）人口规模在六个人口的两年期Sabatia angularis以及目前的新模型的近交抑郁症和杂种优势在不同的人口情况下不平衡（碎片化，瓶颈，干扰）。我们的实验研究揭示了高的平均近交衰退和杂种优势的群体。在我们的小样本中，正如预测的那样，杂种优势随着He而下降，而近交衰退并不随着He而变化，实际上随着Nc而下降。我们的理论研究结果表明，近交衰退和杂种优势水平可以在不平衡的人群中有很大的不同，尽管类似的他和强调，联合人口和遗传动力学是关键的预测模式的遗传负荷在非平衡系统。
Inbreeding depression is a major driver of mating system evolution and has critical implications for population viability. Theoretical and empirical attention has been paid to predicting how inbreeding depression varies with population size. Lower inbreeding depression is predicted in small populations at equilibrium, primarily due to higher inbreeding rates facilitating purging and/or fixation of deleterious alleles (drift load), but predictions at demographic and genetic disequilibrium are less clear. In this study, we experimentally evaluate how lifetime inbreeding depression and drift load, estimated by heterosis, vary with census (Nc) and effective (estimated as genetic diversity, He) population size across six populations of the biennial Sabatia angularis as well as present novel models of inbreeding depression and heterosis under varying demographic scenarios at disequilibrium (fragmentation, bottlenecks, disturbances). Our experimental study reveals high average inbreeding depression and heterosis across populations. Across our small sample, heterosis declined with He, as predicted, whereas inbreeding depression did not vary with He and actually decreased with Nc. Our theoretical results demonstrate that inbreeding depression and heterosis levels can vary widely across populations at disequilibrium despite similar He and highlight that joint demographic and genetic dynamics are key to predicting patterns of genetic load in nonequilibrium systems.