CCMAR, CIMAR-Laboratório Associado, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal.
Mol Ecol. 2011 Jun;20(12):2543-54. doi: 10.1111/j.1365-294X.2011.05117.x. Epub 2011 May 3.
Ocean currents are expected to be the predominant environmental factor influencing the dispersal of planktonic larvae or spores; yet, their characterization as predictors of marine connectivity has been hindered by a lack of understanding of how best to use oceanographic data. We used a high-resolution oceanographic model output and Lagrangian particle simulations to derive oceanographic distances (hereafter called transport times) between sites studied for Macrocystis pyrifera genetic differentiation. We build upon the classical isolation-by-distance regression model by asking how much additional variability in genetic differentiation is explained when adding transport time as predictor. We explored the extent to which gene flow is dependent upon seasonal changes in ocean circulation. Because oceanographic transport between two sites is inherently asymmetric, we also compare the explanatory power of models using the minimum or the mean transport times. Finally, we compare the direction of connectivity as estimated by the oceanographic model and genetic assignment tests. We show that the minimum transport time had higher explanatory power than the mean transport time, revealing the importance of considering asymmetry in ocean currents when modelling gene flow. Genetic assignment tests were much less effective in determining asymmetry in gene flow. Summer-derived transport times, in particular for the month of June, which had the strongest current speed, greatest asymmetry and highest spore production, resulted in the best-fit model explaining twice the variability in genetic differentiation relative to models that use geographic distance or habitat continuity. The best overall model also included habitat continuity and explained 65% of the variation in genetic differentiation among sites.
洋流预计是影响浮游幼虫或孢子扩散的主要环境因素；然而，由于缺乏了解如何最好地利用海洋学数据，它们作为海洋连通性预测因子的特征一直受到阻碍。我们使用高分辨率海洋模式输出和拉格朗日粒子模拟来推导 Macrocystis pyrifera 遗传分化研究地点之间的海洋距离（以下称为传输时间）。我们在经典的隔离距离回归模型的基础上，询问当添加传输时间作为预测因子时，遗传分化的额外可变性解释了多少。我们探讨了基因流动在多大程度上取决于海洋环流的季节性变化。由于两个地点之间的海洋输运本质上是不对称的，我们还比较了使用最小或平均传输时间的模型的解释能力。最后，我们比较了海洋学模型和遗传分配测试估计的连通方向。我们表明，最小传输时间比平均传输时间具有更高的解释能力，揭示了在模拟基因流动时考虑洋流不对称性的重要性。遗传分配测试在确定基因流动的不对称性方面效果要差得多。特别是 6 月份夏季传输时间，具有最强的海流速度、最大的不对称性和最高的孢子产量，产生了最佳拟合模型，与使用地理距离或栖息地连续性的模型相比，解释了遗传分化变化的两倍。最佳整体模型还包括栖息地连续性，解释了 65%的站点间遗传分化变化。
