Ecological differentiation between lineages is widely considered to be an important driver of speciation, but support for this hypothesis is mainly derived from the detailed study of a select set of model species pairs. Mounting evidence from nonmodel taxa, meanwhile, suggests that speciation often occurs with minimal differentiation in ecology or ecomorphology, calling into question the true contribution of divergent adaptation to species richness in nature. To better understand divergent ecological adaptation and its role in speciation generally, researchers require a comparative approach that can distinguish its signature from alternative processes, such as drift and parallel selection, in data sets containing many species pairs. Here we introduce new statistical models of divergent adaptation in the continuous traits of paired lineages. In these models, ecomorphological characters diverge as two lineages adapt toward alternative phenotypic optima following their departure from a common ancestor. The absolute distance between optima measures the extent of divergent selection and provides a basis for interpretation. We encode the models in the new R package diverge and extend them to allow the distance between optima to vary across continuous and categorical variables. We test model performance using simulation and demonstrate model application using published data sets of trait divergence in birds and mammals. Our framework provides the first explicit test for signatures of divergent selection in trait divergence data sets, and it will enable empiricists from a wide range of fields to better understand the dynamics of divergent adaptation and its prevalence in nature beyond just our best-studied model systems.

中文翻译：

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发散适应的比较测试：从功能特质发散推断物种形成驱动因素。

人们普遍认为，世系之间的生态分化是物种形成的重要驱动力，但对该假设的支持主要来自对一组特定模型物种对的详细研究。同时，来自非模型分类单元的越来越多的证据表明，物种形成通常在生态学或生态形态学差异最小的情况下发生，这使人们对自然界物种丰富度的不同适应的真正贡献产生了疑问。为了更好地理解不同的生态适应及其在物种形成中的作用，研究人员需要一种比较方法，该方法可以在包含许多物种对的数据集中将其特征与替代过程（例如漂移和平行选择）区分开。在这里，我们介绍配对谱系连续性状中发散适应的新统计模型。在这些模型中，生态形态特征有所不同，因为两个谱系在脱离共同祖先后会适应替代的表型最优。最优值之间的绝对距离度量了发散选择的程度，并为解释提供了基础。我们使用新的R包对模型进行编码发散并扩展它们，以使最优值之间的距离在连续变量和分类变量之间变化。我们使用仿真测试模型性能，并使用已发表的鸟类和哺乳动物性状差异数据集演示模型应用。我们的框架为特质差异数据集中的差异选择的特征提供了第一个显式测试，它将使来自广泛领域的经验主义者能够更好地了解差异适应的动力学及其在自然界中的流行，而不仅仅是我们研究最好的模型系统。