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Data from: Identifying biases at different spatial and temporal scales of diversification: a case study in the Neotropical parrotlet genus Forpus

Citation

Smith, Brian Tilston et al. (2012), Data from: Identifying biases at different spatial and temporal scales of diversification: a case study in the Neotropical parrotlet genus Forpus, Dryad, Dataset, https://doi.org/10.5061/dryad.gb122

Abstract

The temporal origins of the extraordinary biodiversity of the Neotropical region are highly debated. Recent empirical work has found support for alternative models on the tempo of speciation in Neotropical species further fuelling the debate. However, relationships within many Neotropical lineages are poorly understood and it is unclear how this uncertainty impacts inferences on the evolution of taxa in the region. We examined the robustness of diversification patterns in the avian genus Forpus by testing whether the use of different units of biodiversity (i.e. biological species and statistically inferred species) impacted diversification rates and inferences regarding important biogeographic breaks in the genus. We found that the best-fit model of diversification for the biological species data set was a declining rate of diversification; whereas a model of constant diversification was the best-fit model for statistically inferred species or subspecies. Moreover, the relative importance of different landscape features in delimiting genetic structure across the landscape varied across data sets with differing units of biodiversity. Patterns based on divergence times among biological species indicated old speciation events across major geographic and river barriers, in contrast, data sets more inclusive of the diversity in Forpus illustrate the role of both old divergence across major landscape features as well as more recent divergences that are possibly attributed to Pleistocene climatic changes. Overall, these results indicate that conflicting models on the temporal origins of Neotropical birds may be attributable to sampling biases.

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