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Data from: A comparative test for divergent adaptation: inferring speciation drivers from functional trait divergence

Citation

Anderson, Sean A. S.; Weir, Jason T. (2020), Data from: A comparative test for divergent adaptation: inferring speciation drivers from functional trait divergence, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf1xc

Abstract

Ecological differentiation between lineages is widely considered to be an important driver of speciation. However, support for this hypothesis is largely derived from the detailed study of a select set of model species pairs. Mounting evidence from non-model taxa, meanwhile, suggests that speciation often occurs with minimal differentiation in ecomorphology, calling into question the true contribution of divergent selection to species richness in nature. To better understand divergent 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 datasets containing many species pairs. Here we introduce the first 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 datasets of trait divergence in birds and mammals. Our framework provides the first explicit test for the signature of divergent selection in trait divergence datasets, and it will enable empiricists from a wide range of fields to more fully investigate the dynamics of divergent adaptation as well as its prevalence in nature beyond just our best-studied model systems.

Methods

These are previously published data used in the example analyses of the present paper, which is a model description. They are brought together here for the convenience of readers wishing to repeat these analyses.

Funding

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada, Award: 490992