Samarasin Pasan, Shuter Brian J, Wright Stephen I, Rodd F Helen
Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, ON, M5S 3G5, Canada.
Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada.
Conserv Biol. 2017 Feb;31(1):126-135. doi: 10.1111/cobi.12765. Epub 2016 Oct 5.
Accurate understanding of population connectivity is important to conservation because dispersal can play an important role in population dynamics, microevolution, and assessments of extirpation risk and population rescue. Genetic methods are increasingly used to infer population connectivity because advances in technology have made them more advantageous (e.g., cost effective) relative to ecological methods. Given the reductions in wildlife population connectivity since the Industrial Revolution and more recent drastic reductions from habitat loss, it is important to know the accuracy of and biases in genetic connectivity estimators when connectivity has declined recently. Using simulated data, we investigated the accuracy and bias of 2 common estimators of migration (movement of individuals among populations) rate. We focused on the timing of the connectivity change and the magnitude of that change on the estimates of migration by using a coalescent-based method (Migrate-n) and a disequilibrium-based method (BayesAss). Contrary to expectations, when historically high connectivity had declined recently: (i) both methods over-estimated recent migration rates; (ii) the coalescent-based method (Migrate-n) provided better estimates of recent migration rate than the disequilibrium-based method (BayesAss); (iii) the coalescent-based method did not accurately reflect long-term genetic connectivity. Overall, our results highlight the problems with comparing coalescent and disequilibrium estimates to make inferences about the effects of recent landscape change on genetic connectivity among populations. We found that contrasting these 2 estimates to make inferences about genetic-connectivity changes over time could lead to inaccurate conclusions.
准确理解种群连通性对于保护工作至关重要，因为扩散在种群动态、微观进化以及灭绝风险评估和种群救援中都能发挥重要作用。由于技术进步使遗传方法相对于生态方法更具优势（例如成本效益高），因此越来越多地被用于推断种群连通性。鉴于自工业革命以来野生动物种群连通性的下降，以及近期因栖息地丧失而导致的急剧下降，了解在连通性近期下降时遗传连通性估计器的准确性和偏差很重要。我们使用模拟数据研究了两种常见的迁移（个体在种群间的移动）率估计器的准确性和偏差。我们通过基于溯祖的方法（Migrate-n）和基于不平衡的方法（BayesAss），关注连通性变化的时间和变化幅度对迁移估计的影响。与预期相反，当历史上的高连通性近期下降时：（i）两种方法都高估了近期的迁移率；（ii）基于溯祖的方法（Migrate-n）比基于不平衡的方法（BayesAss）能更好地估计近期迁移率；（iii）基于溯祖的方法不能准确反映长期的遗传连通性。总体而言，我们的结果凸显了比较溯祖估计和不平衡估计以推断近期景观变化对种群间遗传连通性影响时存在的问题。我们发现，对比这两种估计以推断遗传连通性随时间的变化可能会得出不准确的结论。
