Evolutionary trends in arvicolids and the endemic murid Mikrotia – New data and a critical overview

Abstract

The study of evolutionary rates dates back to the work of Simpson and Haldane in the 1940s. Small mammals, especially Plio-Pleistocene arvicolids (voles and lemmings), are particularly suited for such studies because they have an unusually complete fossil record and exhibit significant evolutionary change through time. In recent decades, arvicolids have been the focus of intensive research devoted to the tempo and mode of evolutionary change and the identification of trends in dental evolution that can be used to correlate and date fossil sites. These studies have raised interesting questions about whether voles and lemmings had unique evolutionary trajectories, or show convergent evolutionary patterns with other hypsodont rodents. Here we review evolutionary patterns in selected arvicolid lineages and endemic Messinian murids (Mikrotia spp.) and discuss reasons for convergence in dental morphology in these two groups of hypsodont rodents. The results substantiate previously detected patterns, but the larger dataset shows that some trends are less regular than previous studies have suggested. With the exception of a pervasive and sustained trend towards increased hypsodonty, our results show that other features do not follow consistent patterns in all lineages, exhibiting a mosaic pattern comprising stasis, variable rate evolution and gradual unidirectional change through time. Evidence for higher evolutionary rates is found in lineages apparently undergoing adaptations to new ecological niches. In the case of Mikrotia, Microtus voles and the water vole (Mimomys–Arvicola) lineage, a shift to a fossorial lifestyle appears to have been an important driving force in their evolution. For other characters, different causes can be invoked; for example a shift to a semi-aquatic lifestyle may be responsible for the trend towards increasing size in Arvicola. Biochronological application of the data should take into account the complexity and biases of the data.