BackgroundExtinct organisms provide vital information about the time of origination and biogeography of extant groups. The development of phylogenetic methods to study evolutionary processes through time has revolutionized the field of evolutionary biology and led to an unprecedented expansion of our knowledge of the tree of life. Recent developments applying Bayesian approaches, using fossil taxa as tips to be included alongside their living relatives, have revitalized the use of morphological data in evolutionary tree inferences. Eumuroida rodents represent the largest group of mammals including more than a quarter of all extant mammals and have a rich fossil record spanning the last ~ 45 million years. Despite this wealth of data, our current understanding of the classification, major biogeographic patterns, and divergence times for this group comes from molecular phylogenies that use fossils only as a source of node calibrations. However, node calibrations impose several constraints on tree topology and must necessarily make a priori assumptions about the placement of fossil taxa without testing their placement in the tree.ResultsWe present the first morphological dataset with extensive fossil sampling for Muroidea. By applying Bayesian morphological clocks with tip dating and process-based biogeographic models, we provide a novel hypothesis for muroid relationships and revised divergence times for the clade that incorporates uncertainty in the placement of all fossil species. Even under strong violation of the clock model, we found strong congruence between results for divergence times, providing a robust timeline for muroid diversification. This new timeline was used for biogeographic analyses, which revealed a dynamic scenario mostly explained by dispersal events between and within the Palearctic and North African regions.ConclusionsOur results provide important insights into the evolution of Muroidea rodents and clarify the evolutionary pathways of their main lineages. We exploited the advantage of tip dating Bayesian approaches in morphology-based datasets and provided a classification of the largest superfamily of mammals resulting from robust phylogenetic inference, inferring the biogeographical history, diversification, and divergence times of its major lineages.
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