Seascape genetics: A coupled oceanographic-genetic model predicts population structure of Caribbean corals
Seascape genetics: A coupled oceanographic-genetic model predicts population structure of Caribbean corals
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DOI：
10.1016/j.cub.2006.06.052
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发表时间：
2006-08-22
期刊：
影响因子：
9.2
通讯作者：
Palumbi, Stephen R.
中科院分区：
文献类型：
--
作者：
Galindo, Heather M.;Olson, Donald B.;Palumbi, Stephen R.
群体遗传学是衡量海洋种群之间重要的幼体联系的有力工具[1-4]。同样，基于环境数据的海洋学模型可以模拟洋流中的粒子运动，并对种群之间的幼虫联系进行定量估计[5-9]。然而，这两种强大的方法仍然是脱节的，因为目前没有通用模型提供直接比较扩散预测与经验遗传数据的方法（除了，见[10]）。此外，以前的遗传模型考虑了相对简单的扩散情景，这对于海洋幼虫来说往往是不现实的[11-15]，而最近的景观遗传模型尚未应用于海洋环境[16-20]。我们已经开发了一个遗传模型，利用海洋学模型的连接估计，以预测遗传模式造成的幼虫分散在加勒比海珊瑚。然后，我们比较的预测经验数据受到威胁的鹿角珊瑚。我们的海洋学-遗传学耦合模型预测了在本数据集和其他经验数据集中观察到的许多模式;这些模式包括巴哈马群岛的隔离和波多黎各附近的东西向分歧[3，21-23]。这种新方法既为预测海洋种群的遗传结构提供了一种有价值的工具，也为使用经验数据明确测试这些预测提供了一种手段（图1）。
Population genetics is a powerful tool for measuring important larval connections between marine populations [1-4]. Similarly, oceanographic models based on environmental data can simulate particle movements in ocean currents and make quantitative estimates of larval connections between populations possible [5-9]. However, these two powerful approaches have remained disconnected because no general models currently provide a means of directly comparing dispersal predictions with empirical genetic data (except, see [10]). In addition, previous genetic models have considered relatively simple dispersal scenarios that are often unrealistic for marine larvae [11-15], and recent landscape genetic models have yet to be applied in a marine context [16-20]. We have developed a genetic model that uses connectivity estimates from oceanographic models to predict genetic patterns resulting from larval dispersal in a Caribbean coral. We then compare the predictions to empirical data for threatened staghorn corals. Our coupled oceanographic-genetic model predicts many of the patterns observed in this and other empirical datasets; such patterns include the isolation of the Bahamas and an east-west divergence near Puerto Rico [3, 21-23]. This new approach provides both a valuable tool for predicting genetic structure in marine populations and a means of explicitly testing these predictions with empirical data (Figure 1).