Castillo Dean M
Department of Biology Indiana University Bloomington IN USA.
Ecol Evol. 2017 Jun 20;7(15):5808-5820. doi: 10.1002/ece3.3093. eCollection 2017 Aug.
The analysis of large datasets describing reproductive isolation between species has been extremely influential in the study of speciation. However, the statistical methods currently used for these data limit the ability to make direct inferences about the factors predicting the evolution of reproductive isolation. As a result, our understanding of iconic patterns and rules of speciation rely on indirect analyses that have clear statistical limitations. Phylogenetic mixed models are commonly used in ecology and evolution, but have not been applied to studies of reproductive isolation. Here I describe a flexible framework using phylogenetic mixed models to analyze data collected at different evolutionary scales, to test both categorical and continuous predictor variables, and to test the effect of multiple predictors on rates and patterns of reproductive isolation simultaneously. I demonstrate the utility of this framework by re-analyzing four classic datasets, from both animals and plants, and evaluating several hypotheses that could not be tested in the original studies: In the and Bufonidae datasets, I found support for more rapid accumulation of reproductive isolation in sympatric species pairs compared to allopatric species pairs. Using and , I found no evidence supporting the hypothesis that floral differentiation elevates postzygotic reproductive isolation. The faster accumulation of postzygotic isolation in sympatry is likely the result of species coexistence determined by the level of postzygotic isolation between species. In addition, floral trait divergence does not appear to translate into pleiotropic effects on postzygotic reproductive isolation. Overall, these methods can allow researchers to test new hypotheses using a single statistical method, while remedying the statistical limitations of several previous methods.
对描述物种间生殖隔离的大型数据集进行分析，在物种形成研究中极具影响力。然而，目前用于这些数据的统计方法限制了我们直接推断预测生殖隔离进化因素的能力。因此，我们对物种形成标志性模式和规则的理解依赖于存在明显统计局限性的间接分析。系统发育混合模型在生态学和进化研究中常用，但尚未应用于生殖隔离研究。在此，我描述了一个灵活的框架，使用系统发育混合模型来分析在不同进化尺度上收集的数据，以检验分类和连续预测变量，并同时检验多个预测变量对生殖隔离速率和模式的影响。我通过重新分析来自动物和植物的四个经典数据集，并评估一些在原始研究中无法检验的假设，来证明这个框架的实用性：在[数据集名称1]和蟾蜍科数据集里，我发现与异域物种对相比，同域物种对的生殖隔离积累速度更快。使用[数据集名称2]和[数据集名称3]，我没有找到支持花分化会提高合子后生殖隔离这一假设的证据。同域中合子后隔离更快积累可能是由物种间合子后隔离水平决定的物种共存的结果。此外，花性状差异似乎并未转化为对合子后生殖隔离的多效性影响。总体而言，这些方法能让研究人员用单一统计方法检验新假设，同时弥补先前几种方法的统计局限性。
