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Many outstanding questions about speciation are difficult to test empirically because of a lack of suitable study systems. Here, we highlight studies of evolutionary ecology in urban environments to argue that cities provide ideal conditions that can be leveraged to study the speciation process. Considering general findings from these studies, we discuss the mechanisms of speciation that are likely to occur in cities. We also discuss fundamental questions about speciation that urban environments are uniquely suited to address, such as those about the earliest stages of divergence or the role of phenotypic plasticity. We conclude that the study of contemporary speciation in urban environments has promise to facilitate discoveries about the process of speciation as it occurs in the Anthropocene.

The climatic conditions of mountain habitats are greatly influenced by topography. Large differences in microclimate occur with small changes in elevation, and this complex interaction is an important determinant of mountain plant distributions. In spite of this, elevation is not often considered as a relevant predictor in species distribution models (SDMs) for mountain plants. Here, we evaluated the importance of including elevation as a predictor in SDMs for mountain plant species. We generated two sets of SDMs for each of 73 plant species that occur in the Pacific Northwest of North America; one set of models included elevation as a predictor variable and the other set did not. AUC scores indicated that omitting elevation as a predictor resulted in a negligible reduction of model performance. However, further analysis revealed that the omission of elevation resulted in large over-predictions of species’ niche breadths—this effect was most pronounced for species that occupy the highest elevations. In addition, the inclusion of elevation as a predictor constrained the effects of other predictors that superficially affected the outcome of the models generated without elevation. Our results demonstrate that the inclusion of elevation as a predictor variable improves the quality of SDMs for high-elevation plant species. Because of the negligible AUC score penalty for over-predicting niche breadth, our results support the notion that AUC scores alone should not be used as a measure of model quality. More generally, our results illustrate the importance of selecting biologically relevant predictor variables when constructing SDMs.