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Data from: Factors contributing to the accumulation of reproductive isolation: A mixed model approach

Cite this dataset

Castillo, Dean M. (2018). Data from: Factors contributing to the accumulation of reproductive isolation: A mixed model approach [Dataset]. Dryad.


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 Drosophila and Bufonidae datasets, I found support for more rapid accumulation of reproductive isolation in sympatric species pairs compared to allopatric species pairs. Using Silene and Nolana, 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.

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National Science Foundation, Award: MCB-1127059