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Molecular claims of Gondwanan age for Australian agamid lizards are untenable
Andrew F Hugall1, Michael S Y Lee
1Centre for Evolutionary Biology and Biodiversity, School of Earth and Environmental Sciences, University of Adelaide, The South Australian Museum, Adelaide, Australia. Andrew.Hugall@adelaide.edu.au
Abstract:
A recent mtDNA study proposes a surprisingly deep (approximately 150 MYA) divergence between SE Asian and Australasian agamid lizards, consistent with ancient Gondwanan vicariance rather than dispersal across the Indonesian Archipelago. However, the analysis contains a fundamental error: use of rates of molecular evolution inferred from uncorrected sequence divergence to put a time frame on a tree with branch lengths greatly elongated by complex likelihood and rate-smoothing models. Furthermore, this date implies that basal splits within agamids occurred implausibly early, at least 300 MYA (100 Myr before the first fossil lizards and coincident with the earliest fossil reptiles). Analyses of the mtDNA data using more appropriate methods and new information from nuclear (c-mos) sequences suggest a much more recent divergence between SE Asian and Australian agamids (around 30 MYA). Using two fossil boundary dates, bootstrapping the c-mos data gives a 95% confidence interval for this divergence time that is sufficiently recent (14-41 MYA) to exclude an ancient Gondwanan vicariance and is more consistent with Miocene over-water dispersal. As with the mtDNA, the c-mos data implies implausibly old basal divergences among agamids if a Gondwanan age is assumed for the Australasian clade. The analyses also highlight how methods for creating ultrametric trees (especially nonparametric rate smoothing) can greatly modify branch lengths and, thus, always require internal calibrations. The errors associated with inferred dates in the previous study (inferred through parametric bootstrapping) were also unjustifiably low, as this method only considers stochasticity in the substitution model and ignores much larger sources of uncertainty, such as variation in character sampling, tree topology, and calibration accuracy.
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