Department of Biology, Box 90338, Duke University, Durham, NC 27708, USA.
Mol Phylogenet Evol. 2011 May;59(2):523-37. doi: 10.1016/j.ympev.2011.01.020. Epub 2011 Feb 23.
The field of phylogeography continues to grow in terms of power and accessibility. Initially uniting population genetics and phylogenetics, it now spans disciplines as diverse as geology, statistics, climatology, ecology, physiology, and bioinformatics to name a few. One major and recent integration driving the field forward is between "statistical phylogeography" and Geographic Information Systems (GIS) (Knowles, 2009). Merging genetic and geospatial data, and their associated methodological toolkits, is helping to bring explicit hypothesis testing to the field of phylogeography. Hypotheses derived from one approach can be reciprocally tested with data derived from the other field and the synthesis of these data can help place demographic events in an historical and spatial context, guide genetic sampling, and point to areas for further investigation. Here, we present three practical examples of empirical analysis that integrate statistical genetic and GIS tools to construct and test phylogeographic hypotheses. Insights into the evolutionary mechanisms underlying recent divergences can benefit from simultaneously considering diverse types of information to iteratively test and reformulate hypotheses. Our goal is to provide the reader with an introduction to the variety of available tools and their potential application to typical questions in phylogeography with the hope that integrative methods will be more broadly and commonly applied to other biological systems and data sets.
系统发生地理学领域在功能和易用性方面不断发展。它最初将群体遗传学和系统发生学联合起来,现在跨越了地质学、统计学、气候学、生态学、生理学和生物信息学等多个学科。最近,一个推动该领域发展的主要整合是“统计系统发生地理学”和地理信息系统(GIS)之间的整合(Knowles,2009)。合并遗传和地理空间数据及其相关方法工具集,有助于使系统发生地理学领域的假设检验更加明确。从一种方法中得出的假设可以与从另一个领域中得出的数据进行相互检验,这些数据的综合可以帮助将人口统计学事件置于历史和空间背景中,指导遗传采样,并指出进一步研究的方向。在这里,我们提出了三个实际的实证分析示例,这些示例整合了统计遗传和 GIS 工具,以构建和检验系统发生地理学假设。通过同时考虑多种类型的信息来迭代地检验和重新制定假设,可以深入了解最近分歧的进化机制。我们的目标是为读者提供对各种可用工具的介绍,以及它们在系统发生地理学中典型问题上的潜在应用,希望整合方法将更广泛和普遍地应用于其他生物系统和数据集。