| Summary: | Ancient DNA sequences are able to offer valuable insights into molecular evolutionary processes, which are not directly accessible via modern DNA. They are particularly suitable for the estimation of substitution rates because their ages provide calibrating information in phylogenetic analyses, circumventing the difficult task of choosing independent calibration points. The substitution rates obtained from such datasets have typically been high, falling between the rates estimated from pedigrees and species phylogenies. Many of these estimates have been made using a Bayesian phylogenetic method that explicitly accommodates heterochronous data. Stimulated by recent criticism of this method, we present a comprehensive simulation study that validates its performance. For datasets of moderate si ze, i t produces accurate estimates of rates, while appearing robust to assumpti ons about demographic history. We then analyse a large collection of 749 ancient and 727 modern DNA sequences from 19 species of animals, plants and bacteria. Our new estimates confirm that the substitution rates estimated from ancient DNA sequences are elevated above long-term phylogenetic levels. Nene Data Set nene_mt.const.xml Bison Data Set bison.bsp.xml Cave Bear Data Set cavebear.const.xml Brown Bear Data Set brownbear.const.xml Bowhead Whale Data Set bowheadwhale.const.xml Cave Lion Data Set cavelion.const.xml Cow Data Set cow.bsp.xml Cave Hyaena Data Set cavehyaena.bsp.xml Muskox Data Set muskox.const.xml Maize Data Set maize.const.xml Tuco-Tuco Data Set tucotuco.const.xml Horse Data Set horse.expon.xml Woolly Mammoth Data Set woollymammoth.const.xml Arctic Fox Data Set BEAST input files for ancient DNA data sets. Substitution models were selected by comparison of Akaike Information Criterion scores, and coalescent priors were chosen using Bayes Factor analysis. arcticfox.const.xml Adelie Penguin Data Set BEAST input files for ancient DNA data sets. Substitution models were selected by comparison of Akaike Information Criterion scores, ...
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