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Dryad

Testing for adaptive radiation: a new approach applied to Madagascar frogs

Cite this dataset

Moen, Daniel; Ravelojaona, Rojo; Hutter, Carl; Wiens, John (2021). Testing for adaptive radiation: a new approach applied to Madagascar frogs [Dataset]. Dryad. https://doi.org/10.5061/dryad.z08kprr9s

Abstract

Adaptive radiation is a key topic at the intersection of ecology and evolutionary biology. Yet the definition and identification of adaptive radiation both remain contentious. Here, we introduce a new approach for identifying adaptive radiations which combines key aspects of two widely used definitions. Our approach compares evolutionary rates in morphology, performance, and diversification between the candidate radiation and other clades. We then apply this approach to a putative adaptive radiation of frogs from Madagascar (Mantellidae). We collect new data on morphology and performance from mantellid frogs and compare rates of diversification and multivariate evolution of size, shape, and performance between mantellids and other frogs. We find that mantellids potentially pass our test for accelerated rates of evolution for shape, but not for size, performance, or diversification. Our results demonstrate that clades can have accelerated phenotypic evolution without rapid diversification (dubbed “adaptive non-radiation”). We also highlight general issues in testing for adaptive radiation, including taxon sampling and the problem of including another adaptive radiation in the comparison clades. Finally, we suggest that similar tests should be conducted on other putative adaptive radiations on Madagascar, comparing their evolutionary rates to those of related clades outside Madagascar. Based on our results, we speculate that older Madagascar clades may show evolutionary patterns more similar to those on a continent than an island.

Methods

See methods of manuscript.

Funding

National Science Foundation, Award: DEB-1655812

National Science Foundation, Award: DEB-1655690

National Science Foundation, Award: IOS-1942893